U.S. patent number 10,909,313 [Application Number 15/630,462] was granted by the patent office on 2021-02-02 for personalized summary generation of data visualizations.
This patent grant is currently assigned to SAS INSTITUTE INC.. The grantee listed for this patent is SAS Institute Inc.. Invention is credited to Ethem F. Can, Richard W. Crowell, Jared Peterson, Saratendu Sethi, James Tetterton.
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United States Patent |
10,909,313 |
Can , et al. |
February 2, 2021 |
Personalized summary generation of data visualizations
Abstract
Various embodiments are generally directed to systems for
summarizing data visualizations (i.e., images of data
visualizations), such as a graph image, for instance. Some
embodiments are particularly directed to a personalized graph
summarizer that analyzes a data visualization, or image, to detect
pre-defined patterns within the data visualization, and produces a
textual summary of the data visualization based on the pre-defined
patterns detected within the data visualization. In various
embodiments, the personalized graph summarizer may include features
to adapt to the preferences of a user for generating an automated,
personalized computer-generated narrative. For instance, additional
pre-defined patterns may be created for detection and/or the
textual summary may be tailored based on user preferences. In some
such instances, one or more of the user preferences may be
automatically determined by the personalized graph summarizer
without requiring the user to explicitly indicate them. Embodiments
may integrate machine learning and computer vision concepts.
Inventors: |
Can; Ethem F. (Cary, NC),
Crowell; Richard W. (Cary, NC), Tetterton; James (Holly
Springs, NC), Peterson; Jared (Cary, NC), Sethi;
Saratendu (Raleigh, NC) |
Applicant: |
Name |
City |
State |
Country |
Type |
SAS Institute Inc. |
Cary |
NC |
US |
|
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Assignee: |
SAS INSTITUTE INC. (Cary,
NC)
|
Family
ID: |
1000005336926 |
Appl.
No.: |
15/630,462 |
Filed: |
June 22, 2017 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20170371856 A1 |
Dec 28, 2017 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62353222 |
Jun 22, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
40/56 (20200101); G06K 9/469 (20130101); G06K
9/4642 (20130101); G06F 40/186 (20200101); G06K
9/00449 (20130101); G06K 9/6201 (20130101); G06N
20/00 (20190101); G06K 9/6892 (20130101); G06K
9/3233 (20130101); G06F 16/5846 (20190101); G06F
40/194 (20200101); G06K 9/6267 (20130101); G06K
9/4676 (20130101) |
Current International
Class: |
G06F
40/186 (20200101); G06F 16/583 (20190101); G06K
9/62 (20060101); G06K 9/00 (20060101); G06K
9/32 (20060101); G06F 40/56 (20200101); G06F
40/194 (20200101); G06N 20/00 (20190101); G06K
9/68 (20060101); G06K 9/46 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Zhu et al., "Generating Text Description from Content-based
Annotated Image", 2012 International Conference on Systems and
Informatics (ICSAI 2012), 5 pages. cited by applicant .
"Add alternative text to a shape, picture, chart, table, SmartArt
graphic, or other object--Office Support", Jun. 17, 2016, 5 pages
(author unknown). cited by applicant .
"Add text descriptions to data points MATLAB text",
<<http://www.mathworks.com/help/matlab/ref/text.html?requestedDomai-
n=www.mathworks.com>>, Jun. 16, 2017, 16 pages (author
unknown). cited by applicant .
Hirst, Tony, "First Thoughts on Automatically Generating Accessible
Text Descriptions of ggplot Charts in R | Rbloggers",
<<http://www.rbloggers.com/firstthoughtsonautomaticallygeneratingac-
cessibletextdescriptionsofggplotchartsinr/>>, Apr. 29, 2016,
9, pages. cited by applicant .
Chhabra et al., "Generating Text Summaries of Graph Snippets", The
19th International Conference on Management of Data (COMAD), Dec.
19-21, 2013, 4 pages. cited by applicant .
"Narratives for Business Intelligence", Integrated Narratives for
Business Intelligence | Narrative Science,
<<https://www.narrativescience.com/narrativesbusinessintelligence&g-
t;>,Jul. 26, 2017, 9 pages, (author unknown). cited by applicant
.
"R: Add Text to a Plot",
<<https://stat.ethz.ch/Rmanual/Rdevel/library/graphics/html/text.ht-
ml>>, Jun. 17, 2016, 3 pages, author unknown. cited by
applicant .
"Welcome to the iGraphLite page",
<<http://www.inf.udec.cl/.about.leo/igraph.html>>, Jun.
20, 2017, 3 pages, author unknown. cited by applicant .
Chhabra, Shruti, "Entity-centric Summarization: Generating Text
Summaries for Graph Snippets", International World Wide Web
Conference Committee (IW3C2), WWW'14 Companion, Apr. 7-11, 2014, 5
pages. cited by applicant.
|
Primary Examiner: Serrou; Abedelali
Attorney, Agent or Firm: Kacvinsky Daisak Bluni PLLC
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of priority under 35 U.S.C.
.sctn. 119(e) to U.S. Provisional Application Ser. No. 62/353,222
filed Jun. 22, 2016, the entirety of which is incorporated herein
by reference.
Claims
The invention claimed is:
1. An apparatus comprising a processor and a storage to store
instructions that, when executed by the processor, cause the
processor to perform operations comprising: identify a data
visualization comprising a graph image; determine a set of
graph-type correlation scores for the graph image, the set of
graph-type correlation scores to include a graph-type correlation
score for each graph type of a plurality of graph types, each
graph-type correlation score based on a comparison of at least a
portion of the graph image with one or more graph-type models
associated with each graph type of the plurality of graph types;
evaluate the set of graph-type correlation scores to identify a
graph type of the graph image; retrieve a set of patterns based on
the graph type of the graph image, each pattern in the set of
patterns to include one or more pattern examples; determine a set
of region of interest correlation scores for the graph image based
on matching the one or more pattern examples of each pattern in the
set of patterns with at least a portion of the graph image, the set
of region of interest correlation scores to include at least one
region of interest correlation score for each pattern in the set of
patterns; evaluate the set of region of interest correlation scores
to identify one or more candidate regions of interest of the graph
image, each of the one or more candidate regions of interest to
include a portion of the graph image; retrieve a set of pattern
models based on the set of candidate regions of interest of the
graph image, each candidate region of interest in the set of
candidate regions of interest associated with one pattern model in
the set of pattern models, and each pattern model in the set of
pattern models associated with one pattern in the set of patterns;
compare each candidate region of interest in the set of candidate
regions of interest to an associated pattern model in the set of
pattern models to determine a set of pattern model correlation
scores, the set of pattern model correlation scores to include a
pattern model correlation score for each candidate region of
interest of the one or more candidate regions of interest; identify
one or more detected patterns based on the set of pattern model
correlation scores; retrieve one or more text templates from a
computer-readable storage medium based on the one or more detected
patterns, the one or more text templates to include at least one
portion of text associated with each detected pattern of the one or
more detected patterns, each text template of the one or more text
templates associated with a priority level; arrange the one or more
text templates in an order based on the priority level associated
with each text template to generate a textual description of the
graph image; and generate a personalized summary of the graph image
based on the textual description with the one or more text
templates ordered based on the priority level associated with each
text template.
2. The apparatus of claim 1, wherein the processor is caused to
perform operations comprising: detect a portion of the graph image
with contextual information; extract a textual element from the
portion of the graph image with contextual information; and insert
at least a portion of the textual element extracted from the
portion of the graph image with contextual information into at
least one text template of the one or more text templates to
generate the textual description of the graph image.
3. The apparatus of claim 1, wherein the processor is caused to
perform operations comprising: identify a component of the graph
image based on the graph type; detect a portion of the graph image
with potential contextual information; and determine contextual
information is absent from the portion of the graph image with
potential contextual information based on the component of the
graph image identified based on the graph type.
4. The apparatus of claim 1, matching a pattern example of a
pattern in the set of patterns with at least a portion of the graph
image comprising: overlay at least a portion of the pattern example
on the graph image in a plurality of positions; and compute a
region of interest correlation score in the set of region of
interest correlation scores for each of the plurality of
positions.
5. The apparatus of claim 1, wherein the processor is caused to
perform operations comprising: receive an additional pattern
example; and update a pattern model in the set of pattern models
based on the additional pattern example.
6. The apparatus of claim 1, each pattern model correlation score
to indicate a likelihood of a respective candidate region of
interest of the one or more candidate regions of interest including
an associated pattern.
7. The apparatus of claim 1, wherein the processor is caused to
perform operations comprising: present the one or more text
templates arranged based on the priority level associated with each
template sentence via a user interface; arrange the one or more
text templates in an updated order based on input received via the
user interface; alter a priority level of at least one of the one
or more text templates based on the updated order; and generate the
textual description of the graph image based on the priority level
associated with each text template, the priority level associated
with each text template to include the priority level of the at
least one of the one or more text templates altered based on the
updated order.
8. The apparatus of claim 1, wherein the processor is caused to
perform operations comprising: alter the priority level of a text
template based on the input received via a user interface.
9. The apparatus of claim 1, at least one pattern in the set of
patterns comprising a personalized pattern, wherein the processor
is caused to perform operations comprising: create the personalized
pattern based on one or more example graph images and one or more
pattern examples identified in the example graph images based on
input received via a user interface.
10. The apparatus of claim 9, wherein the processor is caused to
perform operations comprising: associate one or more of a priority
level, a template sentence, or a graph type with the personalized
pattern based on input received via the user interface.
11. A computer-implemented method, comprising: identifying a data
visualization comprising a graph image; determining a set of
graph-type correlation scores for the graph image, the set of
graph-type correlation scores to include a graph-type correlation
score for each graph type of a plurality of graph types, each
graph-type correlation score based on a comparison of at least a
portion of the graph image with one or more graph-type models
associated with each graph type of the plurality of graph types;
evaluating the set of graph-type correlation scores to identify a
graph type of the graph image; retrieving a set of patterns based
on the graph type of the graph image, each pattern in the set of
patterns to include one or more pattern examples; determining a set
of region of interest correlation scores for the graph image based
on matching the one or more pattern examples of each pattern in the
set of patterns with at least a portion of the graph image, the set
of region of interest correlation scores to include at least one
region of interest correlation score for each pattern in the set of
patterns; evaluating the set of region of interest correlation
scores to identify one or more candidate regions of interest of the
graph image, each of the one or more candidate regions of interest
to include a portion of the graph image; retrieving a set of
pattern models based on the set of candidate regions of interest of
the graph image, each candidate region of interest in the set of
candidate regions of interest associated with one pattern model in
the set of pattern models, and each pattern model in the set of
pattern models associated with one pattern in the set of patterns;
comparing each candidate region of interest in the set of candidate
regions of interest to an associated pattern model in the set of
pattern models to determine a set of pattern model correlation
scores, the set of pattern model correlation scores to include a
pattern model correlation score for each candidate region of
interest of the one or more candidate regions of interest;
identifying one or more detected patterns based on the set of
pattern model correlation scores; retrieving one or more text
templates from a computer-readable storage medium based on the one
or more detected patterns, the one or more text templates to
include at least one portion of text associated with each detected
pattern of the one or more detected patterns, each text template of
the one or more text templates associated with a priority level;
arranging the one or more text templates in an order based on the
priority level associated with each text template to generate a
textual description of the graph image; and generating a
personalized summary of the graph image based on the textual
description with the one or more text templates ordered based on
the priority level associated with each text template.
12. The computer-implemented method of claim 11, comprising:
detecting a portion of the graph image with contextual information;
extracting a textual element from the portion of the graph image
with contextual information; and inserting at least a portion of
the textual element extracted from the portion of the graph image
with contextual information into at least one text template of the
one or more text templates to generate the textual description of
the graph image.
13. The computer-implemented method of claim 11, comprising:
identifying a component of the graph image based on the graph type;
detecting a portion of the graph image with potential contextual
information; and determining contextual information is absent from
the portion of the graph image with potential contextual
information based on the component of the graph image identified
based on the graph type.
14. The computer-implemented method of claim 11, matching a pattern
example of a pattern in the set of patterns with at least a portion
of the graph image comprising: overlaying at least a portion of the
pattern example on the graph image in a plurality of positions; and
computing a region of interest correlation score in the set of
region of interest correlation scores for each of the plurality of
positions.
15. The computer-implemented method of claim 11, comprising:
receiving an additional pattern example; and updating a pattern
model in the set of pattern models based on the additional pattern
example.
16. The computer-implemented method of claim 11, each pattern model
correlation score to indicate a likelihood of a respective
candidate region of interest of the one or more candidate regions
of interest including an associated pattern.
17. The computer-implemented method of claim 11, comprising:
presenting the one or more text templates arranged based on the
priority level associated with each template sentence via a user
interface; arranging the one or more text templates in an updated
order based on input received via the user interface; altering a
priority level of at least one of the one or more text templates
based on the updated order; and generating the textual description
of the graph image based on the priority level associated with each
text template, the priority level associated with each text
template to include the priority level of the at least one of the
one or more text templates altered based on the updated order.
18. The computer-implemented method of claim 11, comprising:
altering the priority level of a text template based on the input
received via a user interface.
19. The computer-implemented method of claim 11, wherein at least
one pattern in the set of patterns comprising a personalized
pattern, and comprising creating the personalized pattern based on
one or more example graph images and one or more pattern examples
identified in the example graph images based on input received via
a user interface.
20. The computer-implemented method of claim 19, comprising
associating one or more of a priority level, a template sentence,
or a graph type with the personalized pattern based on input
received via the user interface.
21. A computer-program product tangibly embodied in a
non-transitory machine-readable storage medium, the
computer-program product including instructions operable to cause a
processor to perform operations comprising: identify a data
visualization comprising a graph image; determine a set of
graph-type correlation scores for the graph image, the set of
graph-type correlation scores to include a graph-type correlation
score for each graph type of a plurality of graph types, each
graph-type correlation score based on a comparison of at least a
portion of the graph image with one or more graph-type models
associated with each graph type of the plurality of graph types;
evaluate the set of graph-type correlation scores to identify a
graph type of the graph image; retrieve a set of patterns based on
the graph type of the graph image, each pattern in the set of
patterns to include one or more pattern examples; determine a set
of region of interest correlation scores for the graph image based
on matching the one or more pattern examples of each pattern in the
set of patterns with at least a portion of the graph image, the set
of region of interest correlation scores to include at least one
region of interest correlation score for each pattern in the set of
patterns; evaluate the set of region of interest correlation scores
to identify one or more candidate regions of interest of the graph
image, each of the one or more candidate regions of interest to
include a portion of the graph image; retrieve a set of pattern
models based on the set of candidate regions of interest of the
graph image, each candidate region of interest in the set of
candidate regions of interest associated with one pattern model in
the set of pattern models, and each pattern model in the set of
pattern models associated with one pattern in the set of patterns;
compare each candidate region of interest in the set of candidate
regions of interest to an associated pattern model in the set of
pattern models to determine a set of pattern model correlation
scores, the set of pattern model correlation scores to include a
pattern model correlation score for each candidate region of
interest of the one or more candidate regions of interest; identify
one or more detected patterns based on the set of pattern model
correlation scores; retrieve one or more text templates from a
computer-readable storage medium based on the one or more detected
patterns, the one or more text templates to include at least one
portion of text associated with each detected pattern of the one or
more detected patterns, each text template of the one or more text
templates associated with a priority level; arrange the one or more
text templates in an order based on the priority level associated
with each text template to generate a textual description of the
graph image; and generate a personalized summary of the graph image
based on the textual description with the one or more text
templates ordered based on the priority level associated with each
text template.
22. The computer-program product of claim 21, including
instructions operable to cause the processor to perform operations
comprising: detect a portion of the graph image with contextual
information; extract a textual element from the portion of the
graph image with contextual information; and insert at least a
portion of the textual element extracted from the portion of the
graph image with contextual information into at least one text
template of the one or more text templates to generate the textual
description of the graph image.
23. The computer-program product of claim 21, including
instructions operable to cause the processor to perform operations
comprising: identify a component of the graph image based on the
graph type; detect a portion of the graph image with potential
contextual information; and determine contextual information is
absent from the portion of the graph image with potential
contextual information based on the component of the graph image
identified based on the graph type.
24. The computer-program product of claim 21, wherein to match a
pattern example of a pattern in the set of patterns with at least a
portion of the graph image the computer-program product includes
instructions operable to cause the processor to perform operations
comprising: overlay at least a portion of the pattern example on
the graph image in a plurality of positions; and compute a region
of interest correlation score in the set of region of interest
correlation scores for each of the plurality of positions.
25. The computer-program product of claim 21, including
instructions operable to cause the processor to perform operations
comprising: receive an additional pattern example; and update a
pattern model in the set of pattern models based on the additional
pattern example.
26. The computer-program product of claim 21, each pattern model
correlation score to indicate a likelihood of a respective
candidate region of interest of the one or more candidate regions
of interest including an associated pattern.
27. The computer-program product of claim 21, including
instructions operable to cause the processor to perform operations
comprising: present the one or more text templates arranged based
on the priority level associated with each template sentence via a
user interface; arrange the one or more text templates in an
updated order based on input received via the user interface; alter
a priority level of at least one of the one or more text templates
based on the updated order; and generate the textual description of
the graph image based on the priority level associated with each
text template, the priority level associated with each text
template to include the priority level of the at least one of the
one or more text templates altered based on the updated order.
28. The computer-program product of claim 21, including
instructions operable to cause the processor to perform operations
comprising: alter the priority level of a text template based on
the input received via a user interface.
29. The computer-program product of claim 21, at least one pattern
in the set of patterns comprising a personalized pattern, and the
computer-program product including instructions operable to cause
the processor to perform operations comprising: create the
personalized pattern based on one or more example graph images and
one or more pattern examples identified in the example graph images
based on input received via a user interface.
30. The computer-program product of claim 29, including
instructions operable to cause the processor to perform operations
comprising: associate one or more of a priority level, a template
sentence, or a graph type with the personalized pattern based on
input received via the user interface.
Description
BACKGROUND
Generally, data visualizations may refer to various techniques used
to communicate data or information by encoding it as visual objects
(e.g., points, lines, bars, etc.) contained in graphics. Typically,
a data visualization includes information that has been abstracted
in some schematic form, and may include attributes or variables for
the units of information. For instance, numerical data may be
encoded using dots, lines, or bars, to visually communicate a
quantitative message. Sometimes portions of a data visualization
may include information that may be of particular interest, such as
a spike in values.
SUMMARY
This summary is not intended to identify only key or essential
features of the described subject matter, nor is it intended to be
used in isolation to determine the scope of the described subject
matter. The subject matter should be understood by reference to
appropriate portions of the entire specification of this patent,
any or all drawings, and each claim.
Various embodiments described herein may include an apparatus
comprising a processor and a storage to store instructions that,
when executed by the processor, may cause the processor to perform
operations comprising one or more of: identify a data visualization
comprising a graph image; determine a set of graph-type correlation
scores for the graph image, the set of graph-type correlation
scores to include a graph-type correlation score for each graph
type of a plurality of graph types, each graph-type correlation
score based on a comparison of at least a portion of the graph
image with one or more graph-type models associated with each graph
type of the plurality of graph types; evaluate the set of
graph-type correlation scores to identify a graph type of the graph
image; retrieve a set of patterns based on the graph type of the
graph image, each pattern in the set of patterns to include one or
more pattern examples; determine a set of region of interest
correlation scores for the graph image based on matching the one or
more pattern examples of each pattern in the set of patterns with
at least a portion of the graph image, the set of region of
interest correlation scores to include at least one region of
interest correlation score for each pattern in the set of patterns;
evaluate the set of region of interest correlation scores to
identify one or more candidate regions of interest of the graph
image, each of the one or more candidate regions of interest to
include a portion of the graph image; retrieve a set of pattern
models based on the set of candidate regions of interest of the
graph image, each candidate region of interest in the set of
candidate regions of interest associated with one pattern model in
the set of pattern models, and each pattern model in the set of
pattern models associated with one pattern in the set of patterns;
compare each candidate region of interest in the set of candidate
regions of interest to an associated pattern model in the set of
pattern models to determine a set of pattern model correlation
scores, the set of pattern model correlation scores to include a
pattern model correlation score for each candidate region of
interest of the one or more candidate regions of interest; identify
one or more detected patterns based on the set of pattern model
correlation scores; retrieve one or more text templates based on
the one or more detected patterns, the one or more text templates
to include at least one portion of text associated with each
detected pattern of the one or more detected patterns, each text
template of the one or more text templates associated with a
priority level; arrange the one or more text templates in an order
based on the priority level associated with each text template to
generate a textual description of the graph image; and produce a
personalized summary of the graph image, the summary of the graph
image comprising the graph image and the textual description of the
graph image.
In some embodiments, the processor of the apparatus may be caused
to perform operations comprising one or more of: detect a portion
of the graph image with contextual information; extract a textual
element from the portion of the graph image with contextual
information; and insert at least a portion of the textual element
extracted from the portion of the graph image with contextual
information into at least one text template of the one or more text
templates to generate the textual description of the graph
image.
In one or more embodiments, the processor of the apparatus may be
caused to perform operations comprising one or more of: identify a
component of the graph image based on the graph type; detect a
portion of the graph image with potential contextual information;
and determine contextual information is absent from the portion of
the graph image with potential contextual information based on the
component of the graph image identified based on the graph
type.
In various embodiments, matching a pattern example of a pattern in
the set of patterns with at least a portion of the graph image may
comprise one or more of: overlay at least a portion of the pattern
example on the graph image in a plurality of positions; and compute
a region of interest correlation score in the set of region of
interest correlation scores for each of the plurality of
positions.
In some embodiments, the processor of the apparatus may be caused
to perform operations comprising one or more of: receive an
additional pattern example; and update a pattern model in the set
of pattern models based on the additional pattern example.
In one or more embodiments, each pattern model correlation score
may indicate a likelihood of a respective candidate region of
interest of the one or more candidate regions of interest including
an associated pattern.
In various embodiments, the processor of the apparatus may be
caused to perform operations comprising one or more of: present the
one or more text templates arranged based on the priority level
associated with each template sentence via a user interface;
arrange the one or more text templates in an updated order based on
input received via the user interface; alter a priority level of at
least one of the one or more text templates based on the updated
order; and generate the textual description of the graph image
based on the priority level associated with each text template, the
priority level associated with each text template to include the
priority level of the at least one of the one or more text
templates altered based on the updated order.
In some embodiments, the processor of the apparatus may be caused
to perform operations comprising: alter the priority level of a
text template based on the input received via a user interface.
In one or more embodiments, at least one pattern in the set of
patterns may comprise a personalized pattern. In one or more such
embodiments, the processor of the apparatus may be cause to perform
operations comprising create the personalized pattern based on one
or more example graph images and one or more pattern examples
identified in the example graph images based on input received via
a user interface.
In various embodiments, the processor of the apparatus may be
caused to perform operations comprising associate one or more of a
priority level, a template sentence, or a graph type with the
personalized pattern based on input received via the user
interface.
Some embodiments described herein may include a
computer-implemented method, comprising one or more of: identifying
a data visualization comprising a graph image; determining a set of
graph-type correlation scores for the graph image, the set of
graph-type correlation scores to include a graph-type correlation
score for each graph type of a plurality of graph types, each
graph-type correlation score based on a comparison of at least a
portion of the graph image with one or more graph-type models
associated with each graph type of the plurality of graph types;
evaluating the set of graph-type correlation scores to identify a
graph type of the graph image; retrieving a set of patterns based
on the graph type of the graph image, each pattern in the set of
patterns to include one or more pattern examples; determining a set
of region of interest correlation scores for the graph image based
on matching the one or more pattern examples of each pattern in the
set of patterns with at least a portion of the graph image, the set
of region of interest correlation scores to include at least one
region of interest correlation score for each pattern in the set of
patterns; evaluating the set of region of interest correlation
scores to identify one or more candidate regions of interest of the
graph image, each of the one or more candidate regions of interest
to include a portion of the graph image; retrieving a set of
pattern models based on the set of candidate regions of interest of
the graph image, each candidate region of interest in the set of
candidate regions of interest associated with one pattern model in
the set of pattern models, and each pattern model in the set of
pattern models associated with one pattern in the set of patterns;
comparing each candidate region of interest in the set of candidate
regions of interest to an associated pattern model in the set of
pattern models to determine a set of pattern model correlation
scores, the set of pattern model correlation scores to include a
pattern model correlation score for each candidate region of
interest of the one or more candidate regions of interest;
identifying one or more detected patterns based on the set of
pattern model correlation scores; retrieving one or more text
templates based on the one or more detected patterns, the one or
more text templates to include at least one portion of text
associated with each detected pattern of the one or more detected
patterns, each text template of the one or more text templates
associated with a priority level; arranging the one or more text
templates in an order based on the priority level associated with
each text template to generate a textual description of the graph
image; and generating a personalized summary of the graph image,
the summary of the graph image comprising the graph image and the
textual description of the graph image.
In various embodiments, the computer-implemented method may include
one or more of: detecting a portion of the graph image with
contextual information; extracting a textual element from the
portion of the graph image with contextual information; and
inserting at least a portion of the textual element extracted from
the portion of the graph image with contextual information into at
least one text template of the one or more text templates to
generate the textual description of the graph image.
In one or more embodiments, the computer-implemented method may
include one or more of: identifying a component of the graph image
based on the graph type; detecting a portion of the graph image
with potential contextual information; and determining contextual
information is absent from the portion of the graph image with
potential contextual information based on the component of the
graph image identified based on the graph type.
In some embodiments, matching a pattern example of a pattern in the
set of patterns with at least a portion of the graph image may
comprise one or more of: overlaying at least a portion of the
pattern example on the graph image in a plurality of positions; and
computing a region of interest correlation score in the set of
region of interest correlation scores for each of the plurality of
positions.
In various embodiments, the computer-implemented method may include
one or more of: receiving an additional pattern example; and
updating a pattern model in the set of pattern models based on the
additional pattern example.
In one or more embodiments, each pattern model correlation score
may indicate a likelihood of a respective candidate region of
interest of the one or more candidate regions of interest including
an associated pattern.
In some embodiments, the computer-implemented method may include
one or more of: presenting the one or more text templates arranged
based on the priority level associated with each template sentence
via a user interface; arranging the one or more text templates in
an updated order based on input received via the user interface;
altering a priority level of at least one of the one or more text
templates based on the updated order; and generating the textual
description of the graph image based on the priority level
associated with each text template, the priority level associated
with each text template to include the priority level of the at
least one of the one or more text templates altered based on the
updated order.
In various embodiments, the computer-implemented method may include
altering the priority level of a text template based on the input
received via a user interface.
In one or more embodiments, at least one pattern in the set of
patterns comprising a personalized pattern. In one or more such
embodiments, the computer-implemented method may include creating
the personalized pattern based on one or more example graph images
and one or more pattern examples identified in the example graph
images based on input received via a user interface.
In some embodiments, the computer-implemented method may include
associating one or more of a priority level, a template sentence,
or a graph type with the personalized pattern based on input
received via the user interface.
Various embodiments described herein may include a computer-program
product tangibly embodied in a non-transitory machine-readable
storage medium, the computer-program product including instructions
operable to cause a processor to perform operations comprising one
or more of: identify a data visualization comprising a graph image;
determine a set of graph-type correlation scores for the graph
image, the set of graph-type correlation scores to include a
graph-type correlation score for each graph type of a plurality of
graph types, each graph-type correlation score based on a
comparison of at least a portion of the graph image with one or
more graph-type models associated with each graph type of the
plurality of graph types; evaluate the set of graph-type
correlation scores to identify a graph type of the graph image;
retrieve a set of patterns based on the graph type of the graph
image, each pattern in the set of patterns to include one or more
pattern examples; determine a set of region of interest correlation
scores for the graph image based on matching the one or more
pattern examples of each pattern in the set of patterns with at
least a portion of the graph image, the set of region of interest
correlation scores to include at least one region of interest
correlation score for each pattern in the set of patterns; evaluate
the set of region of interest correlation scores to identify one or
more candidate regions of interest of the graph image, each of the
one or more candidate regions of interest to include a portion of
the graph image; retrieve a set of pattern models based on the set
of candidate regions of interest of the graph image, each candidate
region of interest in the set of candidate regions of interest
associated with one pattern model in the set of pattern models, and
each pattern model in the set of pattern models associated with one
pattern in the set of patterns; compare each candidate region of
interest in the set of candidate regions of interest to an
associated pattern model in the set of pattern models to determine
a set of pattern model correlation scores, the set of pattern model
correlation scores to include a pattern model correlation score for
each candidate region of interest of the one or more candidate
regions of interest; identify one or more detected patterns based
on the set of pattern model correlation scores; retrieve one or
more text templates based on the one or more detected patterns, the
one or more text templates to include at least one portion of text
associated with each detected pattern of the one or more detected
patterns, each text template of the one or more text templates
associated with a priority level; arrange the one or more text
templates in an order based on the priority level associated with
each text template to generate a textual description of the graph
image; and generate a personalized summary of the graph image, the
summary of the graph image comprising the graph image and the
textual description of the graph image.
In some embodiments, the computer-program product may include
instructions operable to cause the processor to perform operations
comprising one or more of: detect a portion of the graph image with
contextual information; extract a textual element from the portion
of the graph image with contextual information; and insert at least
a portion of the textual element extracted from the portion of the
graph image with contextual information into at least one text
template of the one or more text templates to generate the textual
description of the graph image.
In one or more embodiments, the computer-program product may
include instructions operable to cause the processor to perform
operations comprising one or more of: identify a component of the
graph image based on the graph type; detect a portion of the graph
image with potential contextual information; and determine
contextual information is absent from the portion of the graph
image with potential contextual information based on the component
of the graph image identified based on the graph type.
In various embodiments, to match a pattern example of a pattern in
the set of patterns with at least a portion of the graph image, the
computer-program product may include instructions operable to cause
the processor to perform operations comprising one or more of:
overlay at least a portion of the pattern example on the graph
image in a plurality of positions; and compute a region of interest
correlation score in the set of region of interest correlation
scores for each of the plurality of positions.
In some embodiments, the computer-program product may include
instructions operable to cause the processor to perform operations
comprising one or more of: receive an additional pattern example;
and update a pattern model in the set of pattern models based on
the additional pattern example.
In one or more embodiments, each pattern model correlation score
may indicate a likelihood of a respective candidate region of
interest of the one or more candidate regions of interest including
an associated pattern.
In various embodiments, the computer-program product may include
instructions operable to cause the processor to perform operations
comprising one or more of: present the one or more text templates
arranged based on the priority level associated with each template
sentence via a user interface; arrange the one or more text
templates in an updated order based on input received via the user
interface; alter a priority level of at least one of the one or
more text templates based on the updated order; and generate the
textual description of the graph image based on the priority level
associated with each text template, the priority level associated
with each text template to include the priority level of the at
least one of the one or more text templates altered based on the
updated order.
In some embodiments, the computer-program product may include
instructions operable to cause the processor to perform operations
comprising alter the priority level of a text template based on the
input received via a user interface.
In one or more embodiments, at least one pattern in the set of
patterns may comprise a personalized pattern. In one or more such
embodiments, the computer-program product may include instructions
operable to cause the processor to perform operations comprising:
create the personalized pattern based on one or more example graph
images and one or more pattern examples identified in the example
graph images based on input received via a user interface.
In various embodiments, the computer-program product may include
instructions operable to cause the processor to perform operations
comprising: associate one or more of a priority level, a template
sentence, or a graph type with the personalized pattern based on
input received via the user interface.
The foregoing, together with other features and embodiments, will
become more apparent upon referring to the following specification,
claims, and accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
The present disclosure is described in conjunction with the
appended figures:
FIG. 1 illustrates a block diagram that provides an illustration of
the hardware components of a computing system, according to some
embodiments of the present technology.
FIG. 2 illustrates an example network including an example set of
devices communicating with each other over an exchange system and
via a network, according to some embodiments of the present
technology.
FIG. 3 illustrates a representation of a conceptual model of a
communications protocol system, according to some embodiments of
the present technology.
FIG. 4 illustrates a communications grid computing system including
a variety of control and worker nodes, according to some
embodiments of the present technology.
FIG. 5 illustrates a flow chart showing an example process for
adjusting a communications grid or a work project in a
communications grid after a failure of a node, according to some
embodiments of the present technology.
FIG. 6 illustrates a portion of a communications grid computing
system including a control node and a worker node, according to
some embodiments of the present technology.
FIG. 7 illustrates a flow chart showing an example process for
executing a data analysis or processing project, according to some
embodiments of the present technology.
FIG. 8 illustrates a block diagram including components of an Event
Stream Processing Engine (ESPE), according to embodiments of the
present technology.
FIG. 9 illustrates a flow chart showing an example process
including operations performed by an event stream processing
engine, according to some embodiments of the present
technology.
FIG. 10 illustrates an ESP system interfacing between a publishing
device and multiple event subscribing devices, according to
embodiments of the present technology.
FIG. 11A illustrates a flow chart showing an example process for
generating and using a machine-learning model, according to some
embodiments of the present technology.
FIG. 11B illustrates a neural network including multiple layers of
interconnected neurons, according to some embodiments of the
present technology.
FIG. 12A illustrates an embodiment of an exemplary operating
environment for a personalized graph summarizer.
FIG. 12B illustrates an example processing flow of a personalized
graph summarizer.
FIGS. 13A-13H illustrate an example processing flow of a
personalized pattern creator.
FIGS. 14A-14G illustrates an example processing flow of a visual
pattern detector.
FIG. 15 illustrates an example processing flow of a summary
generator.
FIG. 16 illustrates an example processing flow of a context
extractor.
FIG. 17 illustrates an example processing flow of a summary
personalizer.
FIG. 18 illustrates an embodiment of a personalized summary
FIGS. 19A-19B illustrates an embodiment of a logic flow.
DETAILED DESCRIPTION
Various embodiments are generally directed to systems for
summarizing data visualizations (i.e., images of data
visualizations), such as a graph image, for instance. Some
embodiments are particularly directed to a personalized graph
summarizer that analyzes a data visualization, or image, to detect
pre-defined patterns within the data visualization, and produces a
textual summary of the data visualization based on the pre-defined
patterns detected within the data visualization. In various
embodiments, the personalized graph summarizer may include features
to adapt to the preferences of a user, thus providing a
personalized computer-generated narrative. For instance, additional
pre-defined patterns may be created for detection and/or the
textual summary may be tailored based on user preferences. In some
such instances, one or more of the user preferences may be
automatically determined by the personalized graph summarizer
without requiring the user to explicitly indicate them. This and
other embodiments are described and claimed.
Some challenges facing systems for summarizing data visualizations
include the inability to provide a meaningful summary tailored to
the preferences of a user. These challenges may result from the
inputs required by systems to summarize data visualizations. For
example, systems may require annotations of a data visualization as
inputs. In a further example, various systems may require a data
file that includes the underlying data or information to be
communicated by a data visualization in order to summarize the data
visualization. It will be appreciated, as used herein a data
visualization (i.e. image of a data visualization) may include or
refer to image data or an image file (e.g., Joint Photographic
Experts Group (JPEG), Portable Network Graphics (PNG), graphic
interchange format (GIF), Scalable Vector Graphics (SVG), and other
image file formats), however, a data visualization is separate and
distinct from a data file that includes the underlying data or
information to be communicated by the data visualization (e.g.,
Comma-Separated Values (CSV), Extensible Markup Language (XML),
Data Interchange Format (DIF), Excel Binary File Format (XLS), and
similar file formats). For instance, an image file may include
pixel data for displaying an image of a scatter graph, while a data
file may include numerical values corresponding to points in the
scatter graph.
Adding further complexity, the types of data visualizations and the
patterns therein that need to be detected and summarized may vary
among users. For example, industry-specific patterns may need to be
identified and summarized. Further, different emphasis may be
placed on different portions of a data visualization by different
users. For instance, one user may place more emphasis on upward
trends, while another user places more emphasis on downward trends.
These and other factors may result in systems for summarizing data
visualizations with poor performance and limited capabilities. An
additional source of complexity includes the inability to provide
relevant, informative, and/or customized summaries of data
visualizations. For example, some systems may summarize a data
visualizations by merely restating the title of the data
visualization. Such limitations can drastically reduce the
usability and applicability of the data visualization summaries,
contributing to inefficient systems with limited flexibility.
Various embodiments described herein include a personalized graph
summarizer that can generate relevant and useful summaries of data
visualizations without relying on annotations or data files that
include underlying data or information to be communicated by the
data visualization. For instance, the personalized graph summarizer
may generate a natural-language textual summary of a data
visualization based on pre-defined patterns detected in an image
file that comprises the data visualization. In some embodiments,
the personalized graph summarizer may be able to learn additional
types of data visualizations and/or patterns to detect therein. For
example, a personalized graph summarizer may learn to identify and
summarize a candlestick chart. In one or more embodiments, the
personalized graph summarizer may be able to generate and/or tailor
summaries of data visualizations based on user preferences. In one
or more such embodiments, the personalized graph summarizer may
learn user preferences based on interactions of the user with the
personalized graph summarizer. For instance, the personalized graph
summarizer may order one or more sentences in a summary based on
revisions made by the user to a previous summary generated for a
previous data visualization. In various embodiments, the
personalized graph summarizer may include the ability to extract
context from a data visualization. In various such embodiments, the
personalized graph summarizer may tailor a summary of a data
visualization based on context extracted from the data
visualization. For example, axis-labels may be extracted from a
data visualization and used to include units (e.g., dollars, years,
etc.) in a summary of the data visualization. In some embodiments,
a personalized computer-generated narrative can be automatically
generated for one or more data visualizations.
In these and other ways the personalized graph summarizer may
enable customized, efficient, and accurate detection of patterns in
a data visualization to provide relevant and useful summaries of
the data visualization, resulting in several technical effects and
advantages. In various embodiments, the personalized graph
summarizer may be implemented via one or more computing devices,
and thereby provide additional and useful functionality to the one
or more computing devices, resulting in more capable and better
functioning computing devices. For example, the personalized graph
summarizer may enable a computing device to assist the visually
impaired with interpreting and understanding data visualizations.
One or more embodiments can involve computer vision.
With general reference to notations and nomenclature used herein,
portions of the detailed description that follows may be presented
in terms of program procedures executed by a processor of a machine
or of multiple networked machines. These procedural descriptions
and representations are used by those skilled in the art to most
effectively convey the substance of their work to others skilled in
the art. A procedure is here, and generally, conceived to be a
self-consistent sequence of operations leading to a desired result.
These operations are those requiring physical manipulations of
physical quantities. Usually, though not necessarily, these
quantities take the form of electrical, magnetic or optical
communications capable of being stored, transferred, combined,
compared, and otherwise manipulated. It proves convenient at times,
principally for reasons of common usage, to refer to what is
communicated as bits, values, elements, symbols, characters, terms,
numbers, or the like. It should be noted, however, that all of
these and similar terms are to be associated with the appropriate
physical quantities and are merely convenient labels applied to
those quantities.
Further, these manipulations are often referred to in terms, such
as adding or comparing, which are commonly associated with mental
operations performed by a human operator. However, no such
capability of a human operator is necessary, or desirable in most
cases, in any of the operations described herein that form part of
one or more embodiments. Rather, these operations are machine
operations. Useful machines for performing operations of various
embodiments include machines selectively activated or configured by
a routine stored within that is written in accordance with the
teachings herein, and/or include apparatus specially constructed
for the required purpose. Various embodiments also relate to
apparatus or systems for performing these operations. These
apparatuses may be specially constructed for the required purpose
or may include a general-purpose computer. The required structure
for a variety of these machines will appear from the description
given.
Reference is now made to the drawings, wherein like reference
numerals are used to refer to like elements throughout. In the
following description, for purposes of explanation, numerous
specific details are set forth in order to provide a thorough
understanding thereof. It may be evident, however, that the novel
embodiments can be practiced without these specific details. In
other instances, well known structures and devices are shown in
block diagram form in order to facilitate a description thereof.
The intention is to cover all modifications, equivalents, and
alternatives within the scope of the claims.
Systems depicted in some of the figures may be provided in various
configurations. In some embodiments, the systems may be configured
as a distributed system where one or more components of the system
are distributed across one or more networks in a cloud computing
system and/or a fog computing system.
FIG. 1 is a block diagram that provides an illustration of the
hardware components of a data transmission network 100, according
to embodiments of the present technology. Data transmission network
100 is a specialized computer system that may be used for
processing large amounts of data where a large number of computer
processing cycles are required.
Data transmission network 100 may also include computing
environment 114. Computing environment 114 may be a specialized
computer or other machine that processes the data received within
the data transmission network 100. Data transmission network 100
also includes one or more network devices 102. Network devices 102
may include client devices that attempt to communicate with
computing environment 114. For example, network devices 102 may
send data to the computing environment 114 to be processed, may
send signals to the computing environment 114 to control different
aspects of the computing environment or the data it is processing,
among other reasons. Network devices 102 may interact with the
computing environment 114 through a number of ways, such as, for
example, over one or more networks 108. As shown in FIG. 1,
computing environment 114 may include one or more other systems.
For example, computing environment 114 may include a database
system 118 and/or a communications grid 120.
In other embodiments, network devices may provide a large amount of
data, either all at once or streaming over a period of time (e.g.,
using event stream processing (ESP), described further with respect
to FIGS. 8-10), to the computing environment 114 via networks 108.
For example, network devices 102 may include network computers,
sensors, databases, or other devices that may transmit or otherwise
provide data to computing environment 114. For example, network
devices may include local area network devices, such as routers,
hubs, switches, or other computer networking devices. These devices
may provide a variety of stored or generated data, such as network
data or data specific to the network devices themselves. Network
devices may also include sensors that monitor their environment or
other devices to collect data regarding that environment or those
devices, and such network devices may provide data they collect
over time. Network devices may also include devices within the
internet of things, such as devices within a home automation
network. Some of these devices may be referred to as edge devices,
and may involve edge computing circuitry. Data may be transmitted
by network devices directly to computing environment 114 or to
network-attached data stores, such as network-attached data stores
110 for storage so that the data may be retrieved later by the
computing environment 114 or other portions of data transmission
network 100.
Data transmission network 100 may also include one or more
network-attached data stores 110. Network-attached data stores 110
are used to store data to be processed by the computing environment
114 as well as any intermediate or final data generated by the
computing system in non-volatile memory. However, in certain
embodiments, the configuration of the computing environment 114
allows its operations to be performed such that intermediate and
final data results can be stored solely in volatile memory (e.g.,
RAM), without a requirement that intermediate or final data results
be stored to non-volatile types of memory (e.g., disk). This can be
useful in certain situations, such as when the computing
environment 114 receives ad hoc queries from a user and when
responses, which are generated by processing large amounts of data,
need to be generated on-the-fly. In this non-limiting situation,
the computing environment 114 may be configured to retain the
processed information within memory so that responses can be
generated for the user at different levels of detail as well as
allow a user to interactively query against this information.
Network-attached data stores may store a variety of different types
of data organized in a variety of different ways and from a variety
of different sources. For example, network-attached data storage
may include storage other than primary storage located within
computing environment 114 that is directly accessible by processors
located therein. Network-attached data storage may include
secondary, tertiary or auxiliary storage, such as large hard
drives, servers, virtual memory, among other types. Storage devices
may include portable or non-portable storage devices, optical
storage devices, and various other mediums capable of storing,
containing data. A machine-readable storage medium or
computer-readable storage medium may include a non-transitory
medium in which data can be stored and that does not include
carrier waves and/or transitory electronic signals. Examples of a
non-transitory medium may include, for example, a magnetic disk or
tape, optical storage media such as compact disk or digital
versatile disk, flash memory, memory or memory devices. A
computer-program product may include code and/or machine-executable
instructions that may represent a procedure, a function, a
subprogram, a program, a routine, a subroutine, a module, a
software package, a class, or any combination of instructions, data
structures, or program statements. A code segment may be coupled to
another code segment or a hardware circuit by passing and/or
receiving information, data, arguments, parameters, or memory
contents. Information, arguments, parameters, data, etc. may be
passed, forwarded, or transmitted via any suitable means including
memory sharing, message passing, token passing, network
transmission, among others. Furthermore, the data stores may hold a
variety of different types of data. For example, network-attached
data stores 110 may hold unstructured (e.g., raw) data, such as
manufacturing data (e.g., a database containing records identifying
products being manufactured with parameter data for each product,
such as colors and models) or product sales databases (e.g., a
database containing individual data records identifying details of
individual product sales).
The unstructured data may be presented to the computing environment
114 in different forms such as a flat file or a conglomerate of
data records, and may have data values and accompanying time
stamps. The computing environment 114 may be used to analyze the
unstructured data in a variety of ways to determine the best way to
structure (e.g., hierarchically) that data, such that the
structured data is tailored to a type of further analysis that a
user wishes to perform on the data. For example, after being
processed, the unstructured time stamped data may be aggregated by
time (e.g., into daily time period units) to generate time series
data and/or structured hierarchically according to one or more
dimensions (e.g., parameters, attributes, and/or variables). For
example, data may be stored in a hierarchical data structure, such
as a ROLAP OR MOLAP database, or may be stored in another tabular
form, such as in a flat-hierarchy form.
Data transmission network 100 may also include one or more server
farms 106. Computing environment 114 may route select
communications or data to the one or more sever farms 106 or one or
more servers within the server farms. Server farms 106 can be
configured to provide information in a predetermined manner. For
example, server farms 106 may access data to transmit in response
to a communication. Server farms 106 may be separately housed from
each other device within data transmission network 100, such as
computing environment 114, and/or may be part of a device or
system.
Server farms 106 may host a variety of different types of data
processing as part of data transmission network 100. Server farms
106 may receive a variety of different data from network devices,
from computing environment 114, from cloud network 116, or from
other sources. The data may have been obtained or collected from
one or more sensors, as inputs from a control database, or may have
been received as inputs from an external system or device. Server
farms 106 may assist in processing the data by turning raw data
into processed data based on one or more rules implemented by the
server farms. For example, sensor data may be analyzed to determine
changes in an environment over time or in real-time.
Data transmission network 100 may also include one or more cloud
networks 116. Cloud network 116 may include a cloud infrastructure
system that provides cloud services. In certain embodiments,
services provided by the cloud network 116 may include a host of
services that are made available to users of the cloud
infrastructure system on demand Cloud network 116 is shown in FIG.
1 as being connected to computing environment 114 (and therefore
having computing environment 114 as its client or user), but cloud
network 116 may be connected to or utilized by any of the devices
in FIG. 1. Services provided by the cloud network can dynamically
scale to meet the needs of its users. The cloud network 116 may
comprise one or more computers, servers, and/or systems. In some
embodiments, the computers, servers, and/or systems that make up
the cloud network 116 are different from the user's own on-premises
computers, servers, and/or systems. For example, the cloud network
116 may host an application, and a user may, via a communication
network such as the Internet, on demand, order and use the
application.
While each device, server and system in FIG. 1 is shown as a single
device, it will be appreciated that multiple devices may instead be
used. For example, a set of network devices can be used to transmit
various communications from a single user, or remote server 140 may
include a server stack. As another example, data may be processed
as part of computing environment 114.
Each communication within data transmission network 100 (e.g.,
between client devices, between servers 106 and computing
environment 114 or between a server and a device) may occur over
one or more networks 108. Networks 108 may include one or more of a
variety of different types of networks, including a wireless
network, a wired network, or a combination of a wired and wireless
network. Examples of suitable networks include the Internet, a
personal area network, a local area network (LAN), a wide area
network (WAN), or a wireless local area network (WLAN). A wireless
network may include a wireless interface or combination of wireless
interfaces. As an example, a network in the one or more networks
108 may include a short-range communication channel, such as a
Bluetooth or a Bluetooth Low Energy channel. A wired network may
include a wired interface. The wired and/or wireless networks may
be implemented using routers, access points, bridges, gateways, or
the like, to connect devices in the network 114, as will be further
described with respect to FIG. 2. The one or more networks 108 can
be incorporated entirely within or can include an intranet, an
extranet, or a combination thereof. In one embodiment,
communications between two or more systems and/or devices can be
achieved by a secure communications protocol, such as secure
sockets layer (SSL) or transport layer security (TLS). In addition,
data and/or transactional details may be encrypted.
Some aspects may utilize the Internet of Things (IoT), where things
(e.g., machines, devices, phones, sensors) can be connected to
networks and the data from these things can be collected and
processed within the things and/or external to the things. For
example, the IoT can include sensors in many different devices, and
high value analytics can be applied to identify hidden
relationships and drive increased efficiencies. This can apply to
both big data analytics and real-time (e.g., ESP) analytics. This
will be described further below with respect to FIG. 2.
As noted, computing environment 114 may include a communications
grid 120 and a transmission network database system 118.
Communications grid 120 may be a grid-based computing system for
processing large amounts of data. The transmission network database
system 118 may be for managing, storing, and retrieving large
amounts of data that are distributed to and stored in the one or
more network-attached data stores 110 or other data stores that
reside at different locations within the transmission network
database system 118. The compute nodes in the grid-based computing
system 120 and the transmission network database system 118 may
share the same processor hardware, such as processors that are
located within computing environment 114.
FIG. 2 illustrates an example network including an example set of
devices communicating with each other over an exchange system and
via a network, according to embodiments of the present technology.
As noted, each communication within data transmission network 100
may occur over one or more networks. System 200 includes a network
device 204 configured to communicate with a variety of types of
client devices, for example client devices 230, over a variety of
types of communication channels.
As shown in FIG. 2, network device 204 can transmit a communication
over a network (e.g., a cellular network via a base station 210).
The communication can be routed to another network device, such as
network devices 205-209, via base station 210. The communication
can also be routed to computing environment 214 via base station
210. For example, network device 204 may collect data either from
its surrounding environment or from other network devices (such as
network devices 205-209) and transmit that data to computing
environment 214.
Although network devices 204-209 are shown in FIG. 2 as a mobile
phone, laptop computer, tablet computer, temperature sensor, motion
sensor, and audio sensor respectively, the network devices may be
or include sensors that are sensitive to detecting aspects of their
environment. For example, the network devices may include sensors
such as water sensors, power sensors, electrical current sensors,
chemical sensors, optical sensors, pressure sensors, geographic or
position sensors (e.g., GPS), velocity sensors, acceleration
sensors, flow rate sensors, among others. Examples of
characteristics that may be sensed include force, torque, load,
strain, position, temperature, air pressure, fluid flow, chemical
properties, resistance, electromagnetic fields, radiation,
irradiance, proximity, acoustics, moisture, distance, speed,
vibrations, acceleration, electrical potential, electrical current,
among others. The sensors may be mounted to various components used
as part of a variety of different types of systems (e.g., an oil
drilling operation). The network devices may detect and record data
related to the environment that it monitors, and transmit that data
to computing environment 214.
As noted, one type of system that may include various sensors that
collect data to be processed and/or transmitted to a computing
environment according to certain embodiments includes an oil
drilling system. For example, the one or more drilling operation
sensors may include surface sensors that measure a hook load, a
fluid rate, a temperature and a density in and out of the wellbore,
a standpipe pressure, a surface torque, a rotation speed of a drill
pipe, a rate of penetration, a mechanical specific energy, etc. and
downhole sensors that measure a rotation speed of a bit, fluid
densities, downhole torque, downhole vibration (axial, tangential,
lateral), a weight applied at a drill bit, an annular pressure, a
differential pressure, an azimuth, an inclination, a dog leg
severity, a measured depth, a vertical depth, a downhole
temperature, etc. Besides the raw data collected directly by the
sensors, other data may include parameters either developed by the
sensors or assigned to the system by a client or other controlling
device. For example, one or more drilling operation control
parameters may control settings such as a mud motor speed to flow
ratio, a bit diameter, a predicted formation top, seismic data,
weather data, etc. Other data may be generated using physical
models such as an earth model, a weather model, a seismic model, a
bottom hole assembly model, a well plan model, an annular friction
model, etc. In addition to sensor and control settings, predicted
outputs, of for example, the rate of penetration, mechanical
specific energy, hook load, flow in fluid rate, flow out fluid
rate, pump pressure, surface torque, rotation speed of the drill
pipe, annular pressure, annular friction pressure, annular
temperature, equivalent circulating density, etc. may also be
stored in the data warehouse.
In another example, another type of system that may include various
sensors that collect data to be processed and/or transmitted to a
computing environment according to certain embodiments includes a
home automation or similar automated network in a different
environment, such as an office space, school, public space, sports
venue, or a variety of other locations. Network devices in such an
automated network may include network devices that allow a user to
access, control, and/or configure various home appliances located
within the user's home (e.g., a television, radio, light, fan,
humidifier, sensor, microwave, iron, and/or the like), or outside
of the user's home (e.g., exterior motion sensors, exterior
lighting, garage door openers, sprinkler systems, or the like). For
example, network device 102 may include a home automation switch
that may be coupled with a home appliance. In another embodiment, a
network device can allow a user to access, control, and/or
configure devices, such as office-related devices (e.g., copy
machine, printer, or fax machine), audio and/or video related
devices (e.g., a receiver, a speaker, a projector, a DVD player, or
a television), media-playback devices (e.g., a compact disc player,
a CD player, or the like), computing devices (e.g., a home
computer, a laptop computer, a tablet, a personal digital assistant
(PDA), a computing device, or a wearable device), lighting devices
(e.g., a lamp or recessed lighting), devices associated with a
security system, devices associated with an alarm system, devices
that can be operated in an automobile (e.g., radio devices,
navigation devices), and/or the like. Data may be collected from
such various sensors in raw form, or data may be processed by the
sensors to create parameters or other data either developed by the
sensors based on the raw data or assigned to the system by a client
or other controlling device.
In another example, another type of system that may include various
sensors that collect data to be processed and/or transmitted to a
computing environment according to certain embodiments includes a
power or energy grid. A variety of different network devices may be
included in an energy grid, such as various devices within one or
more power plants, energy farms (e.g., wind farm, solar farm, among
others) energy storage facilities, factories, homes and businesses
of consumers, among others. One or more of such devices may include
one or more sensors that detect energy gain or loss, electrical
input or output or loss, and a variety of other efficiencies. These
sensors may collect data to inform users of how the energy grid,
and individual devices within the grid, may be functioning and how
they may be made more efficient.
Network device sensors may also perform processing on data it
collects before transmitting the data to the computing environment
114, or before deciding whether to transmit data to the computing
environment 114. For example, network devices may determine whether
data collected meets certain rules, for example by comparing data
or values computed from the data and comparing that data to one or
more thresholds. The network device may use this data and/or
comparisons to determine if the data should be transmitted to the
computing environment 214 for further use or processing.
Computing environment 214 may include machines 220 and 240.
Although computing environment 214 is shown in FIG. 2 as having two
machines, 220 and 240, computing environment 214 may have only one
machine or may have more than two machines. The machines that make
up computing environment 214 may include specialized computers,
servers, or other machines that are configured to individually
and/or collectively process large amounts of data. The computing
environment 214 may also include storage devices that include one
or more databases of structured data, such as data organized in one
or more hierarchies, or unstructured data. The databases may
communicate with the processing devices within computing
environment 214 to distribute data to them. Since network devices
may transmit data to computing environment 214, that data may be
received by the computing environment 214 and subsequently stored
within those storage devices. Data used by computing environment
214 may also be stored in data stores 235, which may also be a part
of or connected to computing environment 214.
Computing environment 214 can communicate with various devices via
one or more routers 225 or other inter-network or intra-network
connection components. For example, computing environment 214 may
communicate with devices 230 via one or more routers 225. Computing
environment 214 may collect, analyze and/or store data from or
pertaining to communications, client device operations, client
rules, and/or user-associated actions stored at one or more data
stores 235. Such data may influence communication routing to the
devices within computing environment 214, how data is stored or
processed within computing environment 214, among other
actions.
Notably, various other devices can further be used to influence
communication routing and/or processing between devices within
computing environment 214 and with devices outside of computing
environment 214. For example, as shown in FIG. 2, computing
environment 214 may include a web server 240. Thus, computing
environment 214 can retrieve data of interest, such as client
information (e.g., product information, client rules, etc.),
technical product details, news, current or predicted weather, and
so on.
In addition to computing environment 214 collecting data (e.g., as
received from network devices, such as sensors, and client devices
or other sources) to be processed as part of a big data analytics
project, it may also receive data in real time as part of a
streaming analytics environment. As noted, data may be collected
using a variety of sources as communicated via different kinds of
networks or locally. Such data may be received on a real-time
streaming basis. For example, network devices may receive data
periodically from network device sensors as the sensors
continuously sense, monitor and track changes in their
environments. Devices within computing environment 214 may also
perform pre-analysis on data it receives to determine if the data
received should be processed as part of an ongoing project. The
data received and collected by computing environment 214, no matter
what the source or method or timing of receipt, may be processed
over a period of time for a client to determine results data based
on the client's needs and rules.
FIG. 3 illustrates a representation of a conceptual model of a
communications protocol system, according to embodiments of the
present technology. More specifically, FIG. 3 identifies operation
of a computing environment in an Open Systems Interaction model
that corresponds to various connection components. The model 300
shows, for example, how a computing environment, such as computing
environment 314 (or computing environment 214 in FIG. 2) may
communicate with other devices in its network, and control how
communications between the computing environment and other devices
are executed and under what conditions.
The model can include layers 302-314. The layers are arranged in a
stack. Each layer in the stack serves the layer one level higher
than it (except for the application layer, which is the highest
layer), and is served by the layer one level below it (except for
the physical layer, which is the lowest layer). The physical layer
is the lowest layer because it receives and transmits raw bites of
data, and is the farthest layer from the user in a communications
system. On the other hand, the application layer is the highest
layer because it interacts directly with a software
application.
As noted, the model includes a physical layer 302. Physical layer
302 represents physical communication, and can define parameters of
that physical communication. For example, such physical
communication may come in the form of electrical, optical, or
electromagnetic signals. Physical layer 302 also defines protocols
that may control communications within a data transmission
network.
Link layer 304 defines links and mechanisms used to transmit (i.e.,
move) data across a network. The link layer manages node-to-node
communications, such as within a grid computing environment. Link
layer 304 can detect and correct errors (e.g., transmission errors
in the physical layer 302). Link layer 304 can also include a media
access control (MAC) layer and logical link control (LLC)
layer.
Network layer 306 defines the protocol for routing within a
network. In other words, the network layer coordinates transferring
data across nodes in a same network (e.g., such as a grid computing
environment). Network layer 306 can also define the processes used
to structure local addressing within the network.
Transport layer 308 can manage the transmission of data and the
quality of the transmission and/or receipt of that data. Transport
layer 308 can provide a protocol for transferring data, such as,
for example, a Transmission Control Protocol (TCP). Transport layer
308 can assemble and disassemble data frames for transmission. The
transport layer can also detect transmission errors occurring in
the layers below it.
Session layer 310 can establish, maintain, and manage communication
connections between devices on a network. In other words, the
session layer controls the dialogues or nature of communications
between network devices on the network. The session layer may also
establish checkpointing, adjournment, termination, and restart
procedures.
Presentation layer 312 can provide translation for communications
between the application and network layers. In other words, this
layer may encrypt, decrypt and/or format data based on data types
and/or encodings known to be accepted by an application or network
layer.
Application layer 314 interacts directly with software applications
and end users, and manages communications between them. Application
layer 314 can identify destinations, local resource states or
availability and/or communication content or formatting using the
applications.
Intra-network connection components 322 and 324 are shown to
operate in lower levels, such as physical layer 302 and link layer
304, respectively. For example, a hub can operate in the physical
layer, a switch can operate in the physical layer, and a router can
operate in the network layer. Inter-network connection components
326 and 328 are shown to operate on higher levels, such as layers
306-314. For example, routers can operate in the network layer and
network devices can operate in the transport, session,
presentation, and application layers.
As noted, a computing environment 314 can interact with and/or
operate on, in various embodiments, one, more, all or any of the
various layers. For example, computing environment 314 can interact
with a hub (e.g., via the link layer) so as to adjust which devices
the hub communicates with. The physical layer may be served by the
link layer, so it may implement such data from the link layer. For
example, the computing environment 314 may control which devices it
will receive data from. For example, if the computing environment
314 knows that a certain network device has turned off, broken, or
otherwise become unavailable or unreliable, the computing
environment 314 may instruct the hub to prevent any data from being
transmitted to the computing environment 314 from that network
device. Such a process may be beneficial to avoid receiving data
that is inaccurate or that has been influenced by an uncontrolled
environment. As another example, computing environment 314 can
communicate with a bridge, switch, router or gateway and influence
which device within the system (e.g., system 200) the component
selects as a destination. In some embodiments, computing
environment 314 can interact with various layers by exchanging
communications with equipment operating on a particular layer by
routing or modifying existing communications. In another
embodiment, such as in a grid computing environment, a node may
determine how data within the environment should be routed (e.g.,
which node should receive certain data) based on certain parameters
or information provided by other layers within the model.
As noted, the computing environment 314 may be a part of a
communications grid environment, the communications of which may be
implemented as shown in the protocol of FIG. 3. For example,
referring to FIG. 2, one or more of machines 220 and 240 may be
part of a communications grid computing environment. A gridded
computing environment may be employed in a distributed system with
non-interactive workloads where data resides in memory on the
machines, or compute nodes. In such an environment, analytic code,
instead of a database management system, controls the processing
performed by the nodes. Data is co-located by pre-distributing it
to the grid nodes, and the analytic code on each node loads the
local data into memory. Each node may be assigned a particular task
such as a portion of a processing project, or to organize or
control other nodes within the grid.
FIG. 4 illustrates a communications grid computing system 400
including a variety of control and worker nodes, according to
embodiments of the present technology. Communications grid
computing system 400 includes three control nodes and one or more
worker nodes. Communications grid computing system 400 includes
control nodes 402, 404, and 406. The control nodes are
communicatively connected via communication paths 451, 453, and
455. Therefore, the control nodes may transmit information (e.g.,
related to the communications grid or notifications), to and
receive information from each other. Although communications grid
computing system 400 is shown in FIG. 4 as including three control
nodes, the communications grid may include more or less than three
control nodes.
Communications grid computing system (or just "communications
grid") 400 also includes one or more worker nodes. Shown in FIG. 4
are six worker nodes 410-420. Although FIG. 4 shows six worker
nodes, a communications grid according to embodiments of the
present technology may include more or less than six worker nodes.
The number of worker nodes included in a communications grid may be
dependent upon how large the project or data set is being processed
by the communications grid, the capacity of each worker node, the
time designated for the communications grid to complete the
project, among others. Each worker node within the communications
grid 400 may be connected (wired or wirelessly, and directly or
indirectly) to control nodes 402-406. Therefore, each worker node
may receive information from the control nodes (e.g., an
instruction to perform work on a project) and may transmit
information to the control nodes (e.g., a result from work
performed on a project). Furthermore, worker nodes may communicate
with each other (either directly or indirectly). For example,
worker nodes may transmit data between each other related to a job
being performed or an individual task within a job being performed
by that worker node. However, in certain embodiments, worker nodes
may not, for example, be connected (communicatively or otherwise)
to certain other worker nodes. In an embodiment, worker nodes may
only be able to communicate with the control node that controls it,
and may not be able to communicate with other worker nodes in the
communications grid, whether they are other worker nodes controlled
by the control node that controls the worker node, or worker nodes
that are controlled by other control nodes in the communications
grid.
A control node may connect with an external device with which the
control node may communicate (e.g., a grid user, such as a server
or computer, may connect to a controller of the grid). For example,
a server or computer may connect to control nodes and may transmit
a project or job to the node. The project may include a data set.
The data set may be of any size. Once the control node receives
such a project including a large data set, the control node may
distribute the data set or projects related to the data set to be
performed by worker nodes. Alternatively, for a project including a
large data set, the data set may be received or stored by a machine
other than a control node (e.g., a Hadoop data node employing
Hadoop Distributed File System, or HDFS).
Control nodes may maintain knowledge of the status of the nodes in
the grid (i.e., grid status information), accept work requests from
clients, subdivide the work across worker nodes, coordinate the
worker nodes, among other responsibilities. Worker nodes may accept
work requests from a control node and provide the control node with
results of the work performed by the worker node. A grid may be
started from a single node (e.g., a machine, computer, server,
etc.). This first node may be assigned or may start as the primary
control node that will control any additional nodes that enter the
grid.
When a project is submitted for execution (e.g., by a client or a
controller of the grid) it may be assigned to a set of nodes. After
the nodes are assigned to a project, a data structure (i.e., a
communicator) may be created. The communicator may be used by the
project for information to be shared between the project code
running on each node. A communication handle may be created on each
node. A handle, for example, is a reference to the communicator
that is valid within a single process on a single node, and the
handle may be used when requesting communications between
nodes.
A control node, such as control node 402, may be designated as the
primary control node. A server, computer or other external device
may connect to the primary control node. Once the control node
receives a project, the primary control node may distribute
portions of the project to its worker nodes for execution. For
example, when a project is initiated on communications grid 400,
primary control node 402 controls the work to be performed for the
project in order to complete the project as requested or
instructed. The primary control node may distribute work to the
worker nodes based on various factors, such as which subsets or
portions of projects may be completed most efficiently and in the
correct amount of time. For example, a worker node may perform
analysis on a portion of data that is already local (e.g., stored
on) the worker node. The primary control node also coordinates and
processes the results of the work performed by each worker node
after each worker node executes and completes its job. For example,
the primary control node may receive a result from one or more
worker nodes, and the control node may organize (e.g., collect and
assemble) the results received and compile them to produce a
complete result for the project received from the end user.
Any remaining control nodes, such as control nodes 404 and 406, may
be assigned as backup control nodes for the project. In an
embodiment, backup control nodes may not control any portion of the
project. Instead, backup control nodes may serve as a backup for
the primary control node and take over as primary control node if
the primary control node were to fail. If a communications grid
were to include only a single control node, and the control node
were to fail (e.g., the control node is shut off or breaks) then
the communications grid as a whole may fail and any project or job
being run on the communications grid may fail and may not complete.
While the project may be run again, such a failure may cause a
delay (severe delay in some cases, such as overnight delay) in
completion of the project. Therefore, a grid with multiple control
nodes, including a backup control node, may be beneficial.
To add another node or machine to the grid, the primary control
node may open a pair of listening sockets, for example. A socket
may be used to accept work requests from clients, and the second
socket may be used to accept connections from other grid nodes. The
primary control node may be provided with a list of other nodes
(e.g., other machines, computers, servers) that will participate in
the grid, and the role that each node will fill in the grid. Upon
startup of the primary control node (e.g., the first node on the
grid), the primary control node may use a network protocol to start
the server process on every other node in the grid. Command line
parameters, for example, may inform each node of one or more pieces
of information, such as: the role that the node will have in the
grid, the host name of the primary control node, the port number on
which the primary control node is accepting connections from peer
nodes, among others. The information may also be provided in a
configuration file, transmitted over a secure shell tunnel,
recovered from a configuration server, among others. While the
other machines in the grid may not initially know about the
configuration of the grid, that information may also be sent to
each other node by the primary control node. Updates of the grid
information may also be subsequently sent to those nodes.
For any control node, other than the primary control node added to
the grid, the control node may open three sockets. The first socket
may accept work requests from clients, the second socket may accept
connections from other grid members, and the third socket may
connect (e.g., permanently) to the primary control node. When a
control node (e.g., primary control node) receives a connection
from another control node, it first checks to see if the peer node
is in the list of configured nodes in the grid. If it is not on the
list, the control node may clear the connection. If it is on the
list, it may then attempt to authenticate the connection. If
authentication is successful, the authenticating node may transmit
information to its peer, such as the port number on which a node is
listening for connections, the host name of the node, information
about how to authenticate the node, among other information. When a
node, such as the new control node, receives information about
another active node, it will check to see if it already has a
connection to that other node. If it does not have a connection to
that node, it may then establish a connection to that control
node.
Any worker node added to the grid may establish a connection to the
primary control node and any other control nodes on the grid. After
establishing the connection, it may authenticate itself to the grid
(e.g., any control nodes, including both primary and backup, or a
server or user controlling the grid). After successful
authentication, the worker node may accept configuration
information from the control node.
When a node joins a communications grid (e.g., when the node is
powered on or connected to an existing node on the grid or both),
the node is assigned (e.g., by an operating system of the grid) a
universally unique identifier (UUID). This unique identifier may
help other nodes and external entities (devices, users, etc.) to
identify the node and distinguish it from other nodes. When a node
is connected to the grid, the node may share its unique identifier
with the other nodes in the grid. Since each node may share its
unique identifier, each node may know the unique identifier of
every other node on the grid. Unique identifiers may also designate
a hierarchy of each of the nodes (e.g., backup control nodes)
within the grid. For example, the unique identifiers of each of the
backup control nodes may be stored in a list of backup control
nodes to indicate an order in which the backup control nodes will
take over for a failed primary control node to become a new primary
control node. However, a hierarchy of nodes may also be determined
using methods other than using the unique identifiers of the nodes.
For example, the hierarchy may be predetermined, or may be assigned
based on other predetermined factors.
The grid may add new machines at any time (e.g., initiated from any
control node). Upon adding a new node to the grid, the control node
may first add the new node to its table of grid nodes. The control
node may also then notify every other control node about the new
node. The nodes receiving the notification may acknowledge that
they have updated their configuration information.
Primary control node 402 may, for example, transmit one or more
communications to backup control nodes 404 and 406 (and, for
example, to other control or worker nodes within the communications
grid). Such communications may be sent periodically, at fixed time
intervals, between known fixed stages of the project's execution,
among other protocols. The communications transmitted by primary
control node 402 may be of varied types and may include a variety
of types of information. For example, primary control node 402 may
transmit snapshots (e.g., status information) of the communications
grid so that backup control node 404 always has a recent snapshot
of the communications grid. The snapshot or grid status may
include, for example, the structure of the grid (including, for
example, the worker nodes in the grid, unique identifiers of the
nodes, or their relationships with the primary control node) and
the status of a project (including, for example, the status of each
worker node's portion of the project). The snapshot may also
include analysis or results received from worker nodes in the
communications grid. The backup control nodes may receive and store
the backup data received from the primary control node. The backup
control nodes may transmit a request for such a snapshot (or other
information) from the primary control node, or the primary control
node may send such information periodically to the backup control
nodes.
As noted, the backup data may allow the backup control node to take
over as primary control node if the primary control node fails
without requiring the grid to start the project over from scratch.
If the primary control node fails, the backup control node that
will take over as primary control node may retrieve the most recent
version of the snapshot received from the primary control node and
use the snapshot to continue the project from the stage of the
project indicated by the backup data. This may prevent failure of
the project as a whole.
A backup control node may use various methods to determine that the
primary control node has failed. In one example of such a method,
the primary control node may transmit (e.g., periodically) a
communication to the backup control node that indicates that the
primary control node is working and has not failed, such as a
heartbeat communication. The backup control node may determine that
the primary control node has failed if the backup control node has
not received a heartbeat communication for a certain predetermined
period of time. Alternatively, a backup control node may also
receive a communication from the primary control node itself
(before it failed) or from a worker node that the primary control
node has failed, for example because the primary control node has
failed to communicate with the worker node.
Different methods may be performed to determine which backup
control node of a set of backup control nodes (e.g., backup control
nodes 404 and 406) will take over for failed primary control node
402 and become the new primary control node. For example, the new
primary control node may be chosen based on a ranking or
"hierarchy" of backup control nodes based on their unique
identifiers. In an alternative embodiment, a backup control node
may be assigned to be the new primary control node by another
device in the communications grid or from an external device (e.g.,
a system infrastructure or an end user, such as a server or
computer, controlling the communications grid). In another
alternative embodiment, the backup control node that takes over as
the new primary control node may be designated based on bandwidth
or other statistics about the communications grid.
A worker node within the communications grid may also fail. If a
worker node fails, work being performed by the failed worker node
may be redistributed amongst the operational worker nodes. In an
alternative embodiment, the primary control node may transmit a
communication to each of the operable worker nodes still on the
communications grid that each of the worker nodes should
purposefully fail also. After each of the worker nodes fail, they
may each retrieve their most recent saved checkpoint of their
status and re-start the project from that checkpoint to minimize
lost progress on the project being executed.
FIG. 5 illustrates a flow chart showing an example process for
adjusting a communications grid or a work project in a
communications grid after a failure of a node, according to
embodiments of the present technology. The process may include, for
example, receiving grid status information including a project
status of a portion of a project being executed by a node in the
communications grid, as described in operation 502. For example, a
control node (e.g., a backup control node connected to a primary
control node and a worker node on a communications grid) may
receive grid status information, where the grid status information
includes a project status of the primary control node or a project
status of the worker node. The project status of the primary
control node and the project status of the worker node may include
a status of one or more portions of a project being executed by the
primary and worker nodes in the communications grid. The process
may also include storing the grid status information, as described
in operation 504. For example, a control node (e.g., a backup
control node) may store the received grid status information
locally within the control node. Alternatively, the grid status
information may be sent to another device for storage where the
control node may have access to the information.
The process may also include receiving a failure communication
corresponding to a node in the communications grid in operation
506. For example, a node may receive a failure communication
including an indication that the primary control node has failed,
prompting a backup control node to take over for the primary
control node. In an alternative embodiment, a node may receive a
failure that a worker node has failed, prompting a control node to
reassign the work being performed by the worker node. The process
may also include reassigning a node or a portion of the project
being executed by the failed node, as described in operation 508.
For example, a control node may designate the backup control node
as a new primary control node based on the failure communication
upon receiving the failure communication. If the failed node is a
worker node, a control node may identify a project status of the
failed worker node using the snapshot of the communications grid,
where the project status of the failed worker node includes a
status of a portion of the project being executed by the failed
worker node at the failure time.
The process may also include receiving updated grid status
information based on the reassignment, as described in operation
510, and transmitting a set of instructions based on the updated
grid status information to one or more nodes in the communications
grid, as described in operation 512. The updated grid status
information may include an updated project status of the primary
control node or an updated project status of the worker node. The
updated information may be transmitted to the other nodes in the
grid to update their stale stored information.
FIG. 6 illustrates a portion of a communications grid computing
system 600 including a control node and a worker node, according to
embodiments of the present technology. Communications grid 600
computing system includes one control node (control node 602) and
one worker node (worker node 610) for purposes of illustration, but
may include more worker and/or control nodes. The control node 602
is communicatively connected to worker node 610 via communication
path 650. Therefore, control node 602 may transmit information
(e.g., related to the communications grid or notifications), to and
receive information from worker node 610 via path 650.
Similar to in FIG. 4, communications grid computing system (or just
"communications grid") 600 includes data processing nodes (control
node 602 and worker node 610). Nodes 602 and 610 comprise
multi-core data processors. Each node 602 and 610 includes a
grid-enabled software component (GESC) 620 that executes on the
data processor associated with that node and interfaces with buffer
memory 622 also associated with that node. Each node 602 and 610
includes a database management software (DBMS) 628 that executes on
a database server (not shown) at control node 602 and on a database
server (not shown) at worker node 610.
Each node also includes a data store 624. Data stores 624, similar
to network-attached data stores 110 in FIG. 1 and data stores 235
in FIG. 2, are used to store data to be processed by the nodes in
the computing environment. Data stores 624 may also store any
intermediate or final data generated by the computing system after
being processed, for example in non-volatile memory. However, in
certain embodiments, the configuration of the grid computing
environment allows its operations to be performed such that
intermediate and final data results can be stored solely in
volatile memory (e.g., RAM), without a requirement that
intermediate or final data results be stored to non-volatile types
of memory. Storing such data in volatile memory may be useful in
certain situations, such as when the grid receives queries (e.g.,
ad hoc) from a client and when responses, which are generated by
processing large amounts of data, need to be generated quickly or
on-the-fly. In such a situation, the grid may be configured to
retain the data within memory so that responses can be generated at
different levels of detail and so that a client may interactively
query against this information.
Each node also includes a user-defined function (UDF) 626. The UDF
provides a mechanism for the DMBS 628 to transfer data to or
receive data from the database stored in the data stores 624 that
are managed by the DBMS. For example, UDF 626 can be invoked by the
DBMS to provide data to the GESC for processing. The UDF 626 may
establish a socket connection (not shown) with the GESC to transfer
the data. Alternatively, the UDF 626 can transfer data to the GESC
by writing data to shared memory accessible by both the UDF and the
GESC.
The GESC 620 at the nodes 602 and 620 may be connected via a
network, such as network 108 shown in FIG. 1. Therefore, nodes 602
and 620 can communicate with each other via the network using a
predetermined communication protocol such as, for example, the
Message Passing Interface (MPI). Each GESC 620 can engage in
point-to-point communication with the GESC at another node or in
collective communication with multiple GESCs via the network. The
GESC 620 at each node may contain identical (or nearly identical)
software instructions. The GESC at the control node 602 can
communicate, over a communication path 652, with a client deice
630. More specifically, control node 602 may communicate with
client application 632 hosted by the client device 630 to receive
queries and to respond to those queries after processing large
amounts of data.
DMBS 628 may control the creation, maintenance, and use of database
or data structure (not shown) within a node 602 or 610. The
database may organize data stored in data stores 624. The DMBS 628
at control node 602 may accept requests for data and transfer the
appropriate data for the request. With such a process, collections
of data may be distributed across multiple physical locations. In
this example, each node 602 and 610 stores a portion of the total
data managed by the management system in its associated data store
624.
Furthermore, the DBMS may be responsible for protecting against
data loss using replication techniques. Replication includes
providing a backup copy of data stored on one node on one or more
other nodes. Therefore, if one node fails, the data from the failed
node can be recovered from a replicated copy residing at another
node. However, as described herein with respect to FIG. 4, data or
status information for each node in the communications grid may
also be shared with each node on the grid.
FIG. 7 illustrates a flow chart showing an example method for
executing a project within a grid computing system, according to
embodiments of the present technology. As described with respect to
FIG. 6, the GESC at the control node may transmit data with a
client device (e.g., client device 630) to receive queries for
executing a project and to respond to those queries after large
amounts of data have been processed. The query may be transmitted
to the control node, where the query may include a request for
executing a project, as described in operation 702. The query can
contain instructions on the type of data analysis to be performed
in the project and whether the project should be executed using the
grid-based computing environment, as shown in operation 704.
To initiate the project, the control node may determine if the
query requests use of the grid-based computing environment to
execute the project. If the determination is no, then the control
node initiates execution of the project in a solo environment
(e.g., at the control node), as described in operation 710. If the
determination is yes, the control node may initiate execution of
the project in the grid-based computing environment, as described
in operation 706. In such a situation, the request may include a
requested configuration of the grid. For example, the request may
include a number of control nodes and a number of worker nodes to
be used in the grid when executing the project. After the project
has been completed, the control node may transmit results of the
analysis yielded by the grid, as described in operation 708.
Whether the project is executed in a solo or grid-based
environment, the control node provides the results of the
project.
As noted with respect to FIG. 2, the computing environments
described herein may collect data (e.g., as received from network
devices, such as sensors, such as network devices 204-209 in FIG.
2, and client devices or other sources) to be processed as part of
a data analytics project, and data may be received in real time as
part of a streaming analytics environment (e.g., ESP). Data may be
collected using a variety of sources as communicated via different
kinds of networks or locally, such as on a real-time streaming
basis. For example, network devices may receive data periodically
from network device sensors as the sensors continuously sense,
monitor and track changes in their environments. More specifically,
an increasing number of distributed applications develop or produce
continuously flowing data from distributed sources by applying
queries to the data before distributing the data to geographically
distributed recipients. An event stream processing engine (ESPE)
may continuously apply the queries to the data as it is received
and determines which entities should receive the data. Client or
other devices may also subscribe to the ESPE or other devices
processing ESP data so that they can receive data after processing,
based on for example the entities determined by the processing
engine. For example, client devices 230 in FIG. 2 may subscribe to
the ESPE in computing environment 214. In another example, event
subscription devices 874a-c, described further with respect to FIG.
10, may also subscribe to the ESPE. The ESPE may determine or
define how input data or event streams from network devices or
other publishers (e.g., network devices 204-209 in FIG. 2) are
transformed into meaningful output data to be consumed by
subscribers, such as for example client devices 230 in FIG. 2.
FIG. 8 illustrates a block diagram including components of an Event
Stream Processing Engine (ESPE), according to embodiments of the
present technology. ESPE 800 may include one or more projects 802.
A project may be described as a second-level container in an engine
model managed by ESPE 800 where a thread pool size for the project
may be defined by a user. Each project of the one or more projects
802 may include one or more continuous queries 804 that contain
data flows, which are data transformations of incoming event
streams. The one or more continuous queries 804 may include one or
more source windows 806 and one or more derived windows 808.
The ESPE may receive streaming data over a period of time related
to certain events, such as events or other data sensed by one or
more network devices. The ESPE may perform operations associated
with processing data created by the one or more devices. For
example, the ESPE may receive data from the one or more network
devices 204-209 shown in FIG. 2. As noted, the network devices may
include sensors that sense different aspects of their environments,
and may collect data over time based on those sensed observations.
For example, the ESPE may be implemented within one or more of
machines 220 and 240 shown in FIG. 2. The ESPE may be implemented
within such a machine by an ESP application. An ESP application may
embed an ESPE with its own dedicated thread pool or pools into its
application space where the main application thread can do
application-specific work and the ESPE processes event streams at
least by creating an instance of a model into processing
objects.
The engine container is the top-level container in a model that
manages the resources of the one or more projects 802. In an
illustrative embodiment, for example, there may be only one ESPE
800 for each instance of the ESP application, and ESPE 800 may have
a unique engine name. Additionally, the one or more projects 802
may each have unique project names, and each query may have a
unique continuous query name and begin with a uniquely named source
window of the one or more source windows 806. ESPE 800 may or may
not be persistent.
Continuous query modeling involves defining directed graphs of
windows for event stream manipulation and transformation. A window
in the context of event stream manipulation and transformation is a
processing node in an event stream processing model. A window in a
continuous query can perform aggregations, computations,
pattern-matching, and other operations on data flowing through the
window. A continuous query may be described as a directed graph of
source, relational, pattern matching, and procedural windows. The
one or more source windows 806 and the one or more derived windows
808 represent continuously executing queries that generate updates
to a query result set as new event blocks stream through ESPE 800.
A directed graph, for example, is a set of nodes connected by
edges, where the edges have a direction associated with them.
An event object may be described as a packet of data accessible as
a collection of fields, with at least one of the fields defined as
a key or unique identifier (ID). The event object may be created
using a variety of formats including binary, alphanumeric, XML,
etc. Each event object may include one or more fields designated as
a primary identifier (ID) for the event so ESPE 800 can support
operation codes (opcodes) for events including insert, update,
upsert, and delete. Upsert opcodes update the event if the key
field already exists; otherwise, the event is inserted. For
illustration, an event object may be a packed binary representation
of a set of field values and include both metadata and field data
associated with an event. The metadata may include an opcode
indicating if the event represents an insert, update, delete, or
upsert, a set of flags indicating if the event is a normal,
partial-update, or a retention generated event from retention
policy management, and a set of microsecond timestamps that can be
used for latency measurements.
An event block object may be described as a grouping or package of
event objects. An event stream may be described as a flow of event
block objects. A continuous query of the one or more continuous
queries 804 transforms a source event stream made up of streaming
event block objects published into ESPE 800 into one or more output
event streams using the one or more source windows 806 and the one
or more derived windows 808. A continuous query can also be thought
of as data flow modeling.
The one or more source windows 806 are at the top of the directed
graph and have no windows feeding into them. Event streams are
published into the one or more source windows 806, and from there,
the event streams may be directed to the next set of connected
windows as defined by the directed graph. The one or more derived
windows 808 are all instantiated windows that are not source
windows and that have other windows streaming events into them. The
one or more derived windows 808 may perform computations or
transformations on the incoming event streams. The one or more
derived windows 808 transform event streams based on the window
type (that is operators such as join, filter, compute, aggregate,
copy, pattern match, procedural, union, etc.) and window settings.
As event streams are published into ESPE 800, they are continuously
queried, and the resulting sets of derived windows in these queries
are continuously updated.
FIG. 9 illustrates a flow chart showing an example process
including operations performed by an event stream processing
engine, according to some embodiments of the present technology. As
noted, the ESPE 800 (or an associated ESP application) defines how
input event streams are transformed into meaningful output event
streams. More specifically, the ESP application may define how
input event streams from publishers (e.g., network devices
providing sensed data) are transformed into meaningful output event
streams consumed by subscribers (e.g., a data analytics project
being executed by a machine or set of machines).
Within the application, a user may interact with one or more user
interface windows presented to the user in a display under control
of the ESPE independently or through a browser application in an
order selectable by the user. For example, a user may execute an
ESP application, which causes presentation of a first user
interface window, which may include a plurality of menus and
selectors such as drop down menus, buttons, text boxes, hyperlinks,
etc. associated with the ESP application as understood by a person
of skill in the art. As further understood by a person of skill in
the art, various operations may be performed in parallel, for
example, using a plurality of threads.
At operation 900, an ESP application may define and start an ESPE,
thereby instantiating an ESPE at a device, such as machine 220
and/or 240. In an operation 902, the engine container is created.
For illustration, ESPE 800 may be instantiated using a function
call that specifies the engine container as a manager for the
model.
In an operation 904, the one or more continuous queries 804 are
instantiated by ESPE 800 as a model. The one or more continuous
queries 804 may be instantiated with a dedicated thread pool or
pools that generate updates as new events stream through ESPE 800.
For illustration, the one or more continuous queries 804 may be
created to model business processing logic within ESPE 800, to
predict events within ESPE 800, to model a physical system within
ESPE 800, to predict the physical system state within ESPE 800,
etc. For example, as noted, ESPE 800 may be used to support sensor
data monitoring and management (e.g., sensing may include force,
torque, load, strain, position, temperature, air pressure, fluid
flow, chemical properties, resistance, electromagnetic fields,
radiation, irradiance, proximity, acoustics, moisture, distance,
speed, vibrations, acceleration, electrical potential, or
electrical current, etc.).
ESPE 800 may analyze and process events in motion or "event
streams." Instead of storing data and running queries against the
stored data, ESPE 800 may store queries and stream data through
them to allow continuous analysis of data as it is received. The
one or more source windows 806 and the one or more derived windows
808 may be created based on the relational, pattern matching, and
procedural algorithms that transform the input event streams into
the output event streams to model, simulate, score, test, predict,
etc. based on the continuous query model defined and application to
the streamed data.
In an operation 906, a publish/subscribe (pub/sub) capability is
initialized for ESPE 800. In an illustrative embodiment, a pub/sub
capability is initialized for each project of the one or more
projects 802. To initialize and enable pub/sub capability for ESPE
800, a port number may be provided. Pub/sub clients can use a host
name of an ESP device running the ESPE and the port number to
establish pub/sub connections to ESPE 800.
FIG. 10 illustrates an ESP system 850 interfacing between
publishing device 872 and event subscribing devices 874a-c,
according to embodiments of the present technology. ESP system 850
may include ESP device or subsystem 851, event publishing device
872, an event subscribing device A 874a, an event subscribing
device B 874b, and an event subscribing device C 874c. Input event
streams are output to ESP device 851 by publishing device 872. In
alternative embodiments, the input event streams may be created by
a plurality of publishing devices. The plurality of publishing
devices further may publish event streams to other ESP devices. The
one or more continuous queries instantiated by ESPE 800 may analyze
and process the input event streams to form output event streams
output to event subscribing device A 874a, event subscribing device
B 874b, and event subscribing device C 874c. ESP system 850 may
include a greater or a fewer number of event subscribing devices of
event subscribing devices.
Publish-subscribe is a message-oriented interaction paradigm based
on indirect addressing. Processed data recipients specify their
interest in receiving information from ESPE 800 by subscribing to
specific classes of events, while information sources publish
events to ESPE 800 without directly addressing the receiving
parties. ESPE 800 coordinates the interactions and processes the
data. In some cases, the data source receives confirmation that the
published information has been received by a data recipient.
A publish/subscribe API may be described as a library that enables
an event publisher, such as publishing device 872, to publish event
streams into ESPE 800 or an event subscriber, such as event
subscribing device A 874a, event subscribing device B 874b, and
event subscribing device C 874c, to subscribe to event streams from
ESPE 800. For illustration, one or more publish/subscribe APIs may
be defined. Using the publish/subscribe API, an event publishing
application may publish event streams into a running event stream
processor project source window of ESPE 800, and the event
subscription application may subscribe to an event stream processor
project source window of ESPE 800.
The publish/subscribe API provides cross-platform connectivity and
endianness compatibility between ESP application and other
networked applications, such as event publishing applications
instantiated at publishing device 872, and event subscription
applications instantiated at one or more of event subscribing
device A 874a, event subscribing device B 874b, and event
subscribing device C 874c.
Referring back to FIG. 9, operation 906 initializes the
publish/subscribe capability of ESPE 800. In an operation 908, the
one or more projects 802 are started. The one or more started
projects may run in the background on an ESP device. In an
operation 910, an event block object is received from one or more
computing device of the event publishing device 872.
ESP subsystem 800 may include a publishing client 852, ESPE 800, a
subscribing client A 854, a subscribing client B 856, and a
subscribing client C 858. Publishing client 852 may be started by
an event publishing application executing at publishing device 872
using the publish/subscribe API. Subscribing client A 854 may be
started by an event subscription application A, executing at event
subscribing device A 874a using the publish/subscribe API.
Subscribing client B 856 may be started by an event subscription
application B executing at event subscribing device B 874b using
the publish/subscribe API. Subscribing client C 858 may be started
by an event subscription application C executing at event
subscribing device C 874c using the publish/subscribe API.
An event block object containing one or more event objects is
injected into a source window of the one or more source windows 806
from an instance of an event publishing application on event
publishing device 872. The event block object may be generated, for
example, by the event publishing application and may be received by
publishing client 852. A unique ID may be maintained as the event
block object is passed between the one or more source windows 806
and/or the one or more derived windows 808 of ESPE 800, and to
subscribing client A 854, subscribing client B 806, and subscribing
client C 808 and to event subscription device A 874a, event
subscription device B 874b, and event subscription device C 874c.
Publishing client 852 may further generate and include a unique
embedded transaction ID in the event block object as the event
block object is processed by a continuous query, as well as the
unique ID that publishing device 872 assigned to the event block
object.
In an operation 912, the event block object is processed through
the one or more continuous queries 804. In an operation 914, the
processed event block object is output to one or more computing
devices of the event subscribing devices 874a-c. For example,
subscribing client A 804, subscribing client B 806, and subscribing
client C 808 may send the received event block object to event
subscription device A 874a, event subscription device B 874b, and
event subscription device C 874c, respectively.
ESPE 800 maintains the event block containership aspect of the
received event blocks from when the event block is published into a
source window and works its way through the directed graph defined
by the one or more continuous queries 804 with the various event
translations before being output to subscribers. Subscribers can
correlate a group of subscribed events back to a group of published
events by comparing the unique ID of the event block object that a
publisher, such as publishing device 872, attached to the event
block object with the event block ID received by the
subscriber.
In an operation 916, a determination is made concerning whether or
not processing is stopped. If processing is not stopped, processing
continues in operation 910 to continue receiving the one or more
event streams containing event block objects from the, for example,
one or more network devices. If processing is stopped, processing
continues in an operation 918. In operation 918, the started
projects are stopped. In operation 920, the ESPE is shutdown.
As noted, in some embodiments, big data is processed for an
analytics project after the data is received and stored. In other
embodiments, distributed applications process continuously flowing
data in real-time from distributed sources by applying queries to
the data before distributing the data to geographically distributed
recipients. As noted, an event stream processing engine (ESPE) may
continuously apply the queries to the data as it is received and
determines which entities receive the processed data. This allows
for large amounts of data being received and/or collected in a
variety of environments to be processed and distributed in real
time. For example, as shown with respect to FIG. 2, data may be
collected from network devices that may include devices within the
internet of things, such as devices within a home automation
network. However, such data may be collected from a variety of
different resources in a variety of different environments. In any
such situation, embodiments of the present technology allow for
real-time processing of such data.
Aspects of the current disclosure provide technical solutions to
technical problems, such as computing problems that arise when an
ESP device fails which results in a complete service interruption
and potentially significant data loss. The data loss can be
catastrophic when the streamed data is supporting mission critical
operations such as those in support of an ongoing manufacturing or
drilling operation. An embodiment of an ESP system achieves a rapid
and seamless failover of ESPE running at the plurality of ESP
devices without service interruption or data loss, thus
significantly improving the reliability of an operational system
that relies on the live or real-time processing of the data
streams. The event publishing systems, the event subscribing
systems, and each ESPE not executing at a failed ESP device are not
aware of or effected by the failed ESP device. The ESP system may
include thousands of event publishing systems and event subscribing
systems. The ESP system keeps the failover logic and awareness
within the boundaries of out-messaging network connector and
out-messaging network device.
In one example embodiment, a system is provided to support a
failover when event stream processing (ESP) event blocks. The
system includes, but is not limited to, an out-messaging network
device and a computing device. The computing device includes, but
is not limited to, a processor and a computer-readable medium
operably coupled to the processor. The processor is configured to
execute an ESP engine (ESPE). The computer-readable medium has
instructions stored thereon that, when executed by the processor,
cause the computing device to support the failover. An event block
object is received from the ESPE that includes a unique identifier.
A first status of the computing device as active or standby is
determined. When the first status is active, a second status of the
computing device as newly active or not newly active is determined.
Newly active is determined when the computing device is switched
from a standby status to an active status. When the second status
is newly active, a last published event block object identifier
that uniquely identifies a last published event block object is
determined. A next event block object is selected from a
non-transitory computer-readable medium accessible by the computing
device. The next event block object has an event block object
identifier that is greater than the determined last published event
block object identifier. The selected next event block object is
published to an out-messaging network device. When the second
status of the computing device is not newly active, the received
event block object is published to the out-messaging network
device. When the first status of the computing device is standby,
the received event block object is stored in the non-transitory
computer-readable medium.
FIG. 11A is a flow chart of an example of a process for generating
and using a machine-learning model according to some aspects.
Machine learning is a branch of artificial intelligence that
relates to mathematical models that can learn from, categorize, and
make predictions about data. Such mathematical models, which can be
referred to as machine-learning models, can classify input data
among two or more classes; cluster input data among two or more
groups; predict a result based on input data; identify patterns or
trends in input data; identify a distribution of input data in a
space; or any combination of these. Examples of machine-learning
models can include (i) neural networks; (ii) decision trees, such
as classification trees and regression trees; (iii) classifiers,
such as Naive bias classifiers, logistic regression classifiers,
ridge regression classifiers, random forest classifiers, least
absolute shrinkage and selector (LASSO) classifiers, and support
vector machines; (iv) clusterers, such as k-means clusterers,
mean-shift clusterers, and spectral clusterers; (v) factorizers,
such as factorization machines, principal component analyzers and
kernel principal component analyzers; and (vi) ensembles or other
combinations of machine-learning models. In some examples, neural
networks can include deep neural networks, feed-forward neural
networks, recurrent neural networks, convolutional neural networks,
radial basis function (RBF) neural networks, echo state neural
networks, long short-term memory neural networks, bi-directional
recurrent neural networks, gated neural networks, hierarchical
recurrent neural networks, stochastic neural networks, modular
neural networks, spiking neural networks, dynamic neural networks,
cascading neural networks, neuro-fuzzy neural networks, or any
combination of these.
Different machine-learning models may be used interchangeably to
perform a task. Examples of tasks that can be performed at least
partially using machine-learning models include various types of
scoring; bioinformatics; cheminformatics; software engineering;
fraud detection; customer segmentation; generating online
recommendations; adaptive websites; determining customer lifetime
value; search engines; placing advertisements in real time or near
real time; classifying DNA sequences; affective computing;
performing natural language processing and understanding; object
recognition and computer vision; robotic locomotion; playing games;
optimization and metaheuristics; detecting network intrusions;
medical diagnosis and monitoring; or predicting when an asset, such
as a machine, will need maintenance.
Any number and combination of tools can be used to create
machine-learning models. Examples of tools for creating and
managing machine-learning models can include SAS.RTM. Enterprise
Miner, SAS.RTM. Rapid Predictive Modeler, and SAS.RTM. Model
Manager, SAS Cloud Analytic Services (CAS).RTM., SAS Viya.RTM. of
all which are by SAS Institute Inc. of Cary, N.C.
Machine-learning models can be constructed through an at least
partially automated (e.g., with little or no human involvement)
process called training. During training, input data can be
iteratively supplied to a machine-learning model to enable the
machine-learning model to identify patterns related to the input
data or to identify relationships between the input data and output
data. With training, the machine-learning model can be transformed
from an untrained state to a trained state. Input data can be split
into one or more training sets and one or more validation sets, and
the training process may be repeated multiple times. The splitting
may follow a k-fold cross-validation rule, a leave-one-out-rule, a
leave-p-out rule, or a holdout rule. An overview of training and
using a machine-learning model is described below with respect to
the flow chart of FIG. 11A.
In block 1104, training data is received. In some examples, the
training data is received from a remote database or a local
database, constructed from various subsets of data, or input by a
user. The training data can be used in its raw form for training a
machine-learning model or pre-processed into another form, which
can then be used for training the machine-learning model. For
example, the raw form of the training data can be smoothed,
truncated, aggregated, clustered, or otherwise manipulated into
another form, which can then be used for training the
machine-learning model.
In block 1106, a machine-learning model is trained using the
training data. The machine-learning model can be trained in a
supervised, unsupervised, or semi-supervised manner In supervised
training, each input in the training data is correlated to a
desired output. This desired output may be a scalar, a vector, or a
different type of data structure such as text or an image. This may
enable the machine-learning model to learn a mapping between the
inputs and desired outputs. In unsupervised training, the training
data includes inputs, but not desired outputs, so that the
machine-learning model has to find structure in the inputs on its
own. In semi-supervised training, only some of the inputs in the
training data are correlated to desired outputs.
In block 1108, the machine-learning model is evaluated. For
example, an evaluation dataset can be obtained, for example, via
user input or from a database. The evaluation dataset can include
inputs correlated to desired outputs. The inputs can be provided to
the machine-learning model and the outputs from the
machine-learning model can be compared to the desired outputs. If
the outputs from the machine-learning model closely correspond with
the desired outputs, the machine-learning model may have a high
degree of accuracy. For example, if 90% or more of the outputs from
the machine-learning model are the same as the desired outputs in
the evaluation dataset, the machine-learning model may have a high
degree of accuracy. Otherwise, the machine-learning model may have
a low degree of accuracy. The 90% number is an example only. A
realistic and desirable accuracy percentage is dependent on the
problem and the data.
In some examples, if the machine-learning model has an inadequate
degree of accuracy for a particular task, the process can return to
block 1106, where the machine-learning model can be further trained
using additional training data or otherwise modified to improve
accuracy. If the machine-learning model has an adequate degree of
accuracy for the particular task, the process can continue to block
1110.
In block 1110, new data is received. In some examples, the new data
is received from a remote database or a local database, constructed
from various subsets of data, or input by a user. The new data may
be unknown to the machine-learning model. For example, the
machine-learning model may not have previously processed or
analyzed the new data.
In block 1112, the trained machine-learning model is used to
analyze the new data and provide a result. For example, the new
data can be provided as input to the trained machine-learning
model. The trained machine-learning model can analyze the new data
and provide a result that includes a classification of the new data
into a particular class, a clustering of the new data into a
particular group, a prediction based on the new data, or any
combination of these.
In block 1114, the result is post-processed. For example, the
result can be added to, multiplied with, or otherwise combined with
other data as part of a job. As another example, the result can be
transformed from a first format, such as a time series format, into
another format, such as a count series format. Any number and
combination of operations can be performed on the result during
post-processing.
A more specific example of a machine-learning model is the neural
network 1150 shown in FIG. 11B. The neural network 1150 is
represented as multiple layers of interconnected neurons, such as
neuron 1158, that can exchange data between one another. The layers
include an input layer 1152 for receiving input data, a hidden
layer 1154, and an output layer 1156 for providing a result. The
hidden layer 1154 is referred to as hidden because it may not be
directly observable or have its input directly accessible during
the normal functioning of the neural network 1150. Although the
neural network 1150 is shown as having a specific number of layers
and neurons for exemplary purposes, the neural network 1150 can
have any number and combination of layers, and each layer can have
any number and combination of neurons.
The neurons and connections between the neurons can have numeric
weights, which can be tuned during training. For example, training
data can be provided to the input layer 1152 of the neural network
1150, and the neural network 1150 can use the training data to tune
one or more numeric weights of the neural network 1150. In some
examples, the neural network 1150 can be trained using
backpropagation. Backpropagation can include determining a gradient
of a particular numeric weight based on a difference between an
actual output of the neural network 1150 and a desired output of
the neural network 1150. Based on the gradient, one or more numeric
weights of the neural network 1150 can be updated to reduce the
difference, thereby increasing the accuracy of the neural network
1150. This process can be repeated multiple times to train the
neural network 1150. For example, this process can be repeated
hundreds or thousands of times to train the neural network
1150.
In some examples, the neural network 1150 is a feed-forward neural
network. In a feed-forward neural network, every neuron only
propagates an output value to a subsequent layer of the neural
network 1150. For example, data may only move one direction
(forward) from one neuron to the next neuron in a feed-forward
neural network.
In other examples, the neural network 1150 is a recurrent neural
network. A recurrent neural network can include one or more
feedback loops, allowing data to propagate in both forward and
backward through the neural network 1150. This can allow for
information to persist within the recurrent neural network. For
example, a recurrent neural network can determine an output based
at least partially on information that the recurrent neural network
has seen before, giving the recurrent neural network the ability to
use previous input to inform the output.
In some examples, the neural network 1150 operates by receiving a
vector of numbers from one layer; transforming the vector of
numbers into a new vector of numbers using a matrix of numeric
weights, a nonlinearity, or both; and providing the new vector of
numbers to a subsequent layer of the neural network 1150. Each
subsequent layer of the neural network 1150 can repeat this process
until the neural network 1150 outputs a final result at the output
layer 1156. For example, the neural network 1150 can receive a
vector of numbers as an input at the input layer 1152. The neural
network 1150 can multiply the vector of numbers by a matrix of
numeric weights to determine a weighted vector. The matrix of
numeric weights can be tuned during the training of the neural
network 1150. The neural network 1150 can transform the weighted
vector using a nonlinearity, such as a sigmoid tangent or the
hyperbolic tangent. In some examples, the nonlinearity can include
a rectified linear unit, which can be expressed using the following
equation: y=max(x,0) where y is the output and x is an input value
from the weighted vector. The transformed output can be supplied to
a subsequent layer, such as the hidden layer 1154, of the neural
network 1150. The subsequent layer of the neural network 1150 can
receive the transformed output, multiply the transformed output by
a matrix of numeric weights and a nonlinearity, and provide the
result to yet another layer of the neural network 1150. This
process continues until the neural network 1150 outputs a final
result at the output layer 1156.
Other examples of the present disclosure may include any number and
combination of machine-learning models having any number and
combination of characteristics. The machine-learning model(s) can
be trained in a supervised, semi-supervised, or unsupervised
manner, or any combination of these. The machine-learning model(s)
can be implemented using a single computing device or multiple
computing devices, such as the communications grid computing system
400 discussed above.
Implementing some examples of the present disclosure at least in
part by using machine-learning models can reduce the total number
of processing iterations, time, memory, electrical power, or any
combination of these consumed by a computing device when analyzing
data. For example, a neural network may more readily identify
patterns in data than other approaches. This may enable the neural
network to analyze the data using fewer processing cycles and less
memory than other approaches, while obtaining a similar or greater
level of accuracy.
According to embodiments discussed herein, the above-described
computing devices and systems may be utilized to summarize data
visualizations, also referred to as images (e.g., graph images).
The summaries of data visualizations may be used to clearly
communicate relevant parts of data visualizations in an efficient
and effective manner, resulting in a computing device and/or system
with exclusive and advantageous capabilities. For example,
generating a textual summary of a data visualization may enable
information contained in the data visualization to be communicated
to a visually impaired person, such as via a braille terminal. In
another example, a summary of a cardiogram may include
natural-language text that indicates whether or not any patterns
associated with an irregular heartbeat were detected in the cardio
gram.
In some embodiments, the above-described computing devices and
systems may implement a personalized graph summarizer to generate
summaries of data visualizations. In various embodiments, the
personalized graph summarizer may analyze a data visualization to
detect predefined patterns within the data visualization, and
produce a textual summary of the data visualization based on
pre-defined patterns detected within the data visualization. In one
or more embodiments, the personalized graph summarizer may be able
to learn additional types of data visualizations and/or patterns to
detect therein. For example, a personalized graph summarizer may
learn to identify and summarize a spectrogram. In some embodiments,
the personalized graph summarizer may be able to generate and/or
tailor summaries of data visualizations based on user preferences.
In some such embodiments, the personalized graph summarizer may
learn user preferences based on interactions of the user with the
personalized graph summarizer. For instance, the personalized graph
summarizer may assign or alter priority levels associated with one
or more sentences in a summary based on input received from a
user.
In various embodiments, the personalized graph summarizer may
include the ability to extract context from a data visualization.
In various such embodiments, the personalized graph summarizer may
use context extracted from a data visualization to improve clarity
and readability of a natural-language textual summary for the data
visualization. For example, extracted context may alter a sentence
in a summary to read "A spike in dollar amount between Aug. 6, 2015
and Aug. 15, 2015." instead of "A spike in values." based on
context extracted from the data visualization being summarized.
These and other features of the personalized graph summarizer may
enable a computing device and/or system implementing the
personalized graph summarizer to realize unique and advantageous
functionalities, resulting in an improved computer.
FIG. 12A illustrates an example of an operating environment 1200
that may be representative of various embodiments. In operating
environment 1200, system 1205 may receive or identify input 1201
and generate a personalized summary 1204 based on input 1201. For
instance, system 1205 may receive an image file that includes a
graph image as input 1201, analyze the graph image to detect one or
more pre-defined patterns in the graph image, and generate
personalized summary 1204 based on the one or more pre-defined
patterns detected in the graph image. In some embodiments, these
operations may be performed in real-time or near real-time by
system 1205. Further, operating environment 1200 may include a
number of systems, components, devices, and so forth to perform
these operations; however, embodiments are not limited in this
manner In some embodiments, operating environment 1200 may include
more or less systems, components, and devices, for example. In
various embodiments, operating environment 1200 may be implemented
via one or more devices of FIG. 2. Embodiments are not limited in
this context.
In the illustrated embodiments, system 1205 includes a number of
components to generate personalized summary 1204 based on input
1201, including, but not limited to, personalized graph summarizer
(PGS) 1202, memory 1210, storage 1215, processing circuitry 1220,
and one or more interfaces 1225. In various embodiments, system
1205 may be coupled with one or more other systems, components,
devices, networks and so forth, such as via interfaces 1225. In
various such embodiments, the one or more other systems,
components, devices, networks and so forth may perform one or more
functions described herein. For instance, a server may perform one
or more operations of PGS 1202.
Storage 1122 may be any type of storage, including, but not limited
to, magnetic storage and optical storage, for example. In some
instances, storage 1122 may be part of one or more of the storage
systems 1130-1 through 1130-4 and may be a DAS, NAS, or SAN. The
storage 1122 may store information and data for system 1205, such
as information for processing by the by the system 1205. In
embodiments, the storage 1122 may store information, data, one or
more instructions, code, and so forth for PGS 1202.
The memory 1124 of system 1205 can be implemented using any
machine-readable or computer-readable media capable of storing
data, including both volatile and non-volatile memory. In some
embodiments, the machine-readable or computer-readable medium may
include a non-transitory medium. The embodiments are not limited in
this context. The memory 1124 can store data momentarily,
temporarily, or permanently. The memory 1124 stores instructions
and data for system 1205, which may be processed by processing
circuitry 1126. For example, the memory 1124 may also store
temporary variables or other intermediate information while the
processing circuitry 1126 is executing instructions. The memory
1124 is not limited to storing the above discussed data; the memory
1124 may store any type of data. In various embodiments, one or
more portions of PGS 1202 may be stored in memory 1210 and/or
storage 1215. In various such embodiments, PGS 1202 may reside in
storage 1215 and/or memory 1210. In some embodiments, memory 1210
may include random access memory (RAM).
In embodiments, the system 1205 may include processing circuitry
1126 which may include one or more of any type of computational
element, such as but not limited to, a microprocessor, a processor,
central processing unit, digital signal processing unit, dual core
processor, mobile device processor, desktop processor, single core
processor, a system-on-chip (SoC) device, complex instruction set
computing (CISC) microprocessor, a reduced instruction set (RISC)
microprocessor, a very long instruction word (VLIW) microprocessor,
or any other type of processing circuitry, processor or processing
circuit on a single chip or integrated circuit. The processing
circuitry 1126 may be connected to and communicate with the other
elements of the system 1205 including the modeling system 1210, the
storage 1122, the memory 1124, and the one or more interfaces 1220.
In one or more embodiments data associated with PGS 1202 may be
moved from storage 1215 to memory 1210 to provide processing
circuitry 1220 access thereto. For instance, processing circuitry
1220 may perform computations with and/or manipulate data
associated with PGS 1202 that is stored in memory 1210.
The system 1205 may also include one or more interfaces 1220 which
may enable the system to communicate over the network environment
135. In some embodiments, the interfaces 1220 can be a network
interface, a universal serial bus interface (USB), a Firewire
interface, a Small Computer System Interface (SCSI), a parallel
port interface, a serial port interface, a network adapter, a
radio, or any other device to enable the system 1205 to exchange
information. In various embodiments, interfaces 1225 may include
one or more input/output (I/O) devices, such as a display, a touch
screen, a monitor, a keyboard, a mouse, a braille terminal, or any
other devices capable of presenting data to a user or receiving
data from a user. In various such embodiments, one or more I/O
devices may be utilized to receive input 1201 or present
personalized summary 1204.
In various embodiments described herein, PGS 1202 may enable system
1205 to provide a tool that enables users to automatically
summarize data visualizations (i.e., images of data
visualizations). In some embodiments, PGS 1202 may include one or
more features to enable personalization of summaries. In some such
embodiments, PGS 1202 may enable a user to create new personalized
patterns to be searched for within input 1201 and/or tailor text of
personalized summary 1204. In one or more embodiments, PGS 1202 may
include a flexible environment that enables users to interact with
it, such as via one or more of interfaces 1225. For instance, PGS
1202 may include a graphical user interface (GUI) presented on a
display. In various such instances, the GUI 1201 may provide an
interface through which one or more of input 1201 may be received
or personalized summary 1204 may be provided to a user.
Personalization may increase the applicability of PGS 1202,
enabling a user to improve productivity though utilization of PGS
1202.
FIG. 12B illustrates an example of a processing flow 1250 of PGS
1202 that may be representative of various embodiments. In
processing flow 1250, PGS 1202 may include visual pattern detector
1251, personalized pattern creator 1252, summary generator 1254,
summary personalizer 1256, and context extractor 1258. In some
embodiments, visual patter detector 1251 may analyze input 1201 by
detecting pre-defined patterns. In various embodiments,
personalized pattern creator 1252 may enable a user to create or
modify one or more of the pre-defined patterns searched for by
visual pattern detector 1251. In one or more embodiments, summary
generator 1254 may generate and arrange one or more text templates
based on identification of patterns. In some embodiments, context
extractor 1258 may recover data from input 1201, such as via
optical character recognition (OCR). In various embodiments,
summary personalizer 1256 may learn preferences of a user. In one
or more embodiments, summary personalizer 1256 may determine user
preferences via revisions made to a summary by the user.
Embodiments are not limited in this context.
As described above and as will be described in more detail below,
such as with respect to FIGS. 13A-18, the components of PGS 1202
may operate to generate personalized summary 1204 based on input
1201. In embodiments, these operations may include one or more of
the following.
In one or more embodiments, PGS 1202 may identify a data
visualization comprising a graph image. In one or more such
embodiments, the data visualization may include an image or image
file. In various embodiments, PGS 1202 may determine a set of
graph-type correlation scores for the graph images. In some
embodiments, the set of graph-type correlation scores may include a
graph-type correlation score for each graph type of a plurality of
graph types. In various embodiments, each graph-type correlation
score may be based on a comparison of at least a portion of the
graph image with one or more graph-type models associated with each
graph type of the plurality of graph types. In one or more
embodiments, PGS 1202 may evaluate the set of graph-type
correlation scores to identify a graph type of the graph image. In
various embodiments PGS 1202 may retrieve a set of patterns based
on the graph type of the graph image. In some embodiments, each
pattern in the set of patterns may include one or more pattern
examples.
In some embodiments, PGS 1202 may determine a set of region of
interest (ROI) correlation scores for the graph image based on
matching the one or more pattern examples of each pattern in the
set of patterns with at least a portion of the graph image. In
various embodiments, the set of ROI correlation scores may include
at least one ROI correlation score for each pattern in the set of
patterns. In embodiments, PGS 1202 may evaluate the set of ROI
correlation scores to identify one or more candidate ROIs of the
graph image. In one or more embodiments, each of the one or more
candidate ROIs may include a portion of the graph image. In some
embodiments, PGS 1202 may overlay the pattern example of the graph
image in a plurality of positions to match a pattern example of a
pattern in the set of patterns with at least a portion of the graph
image. In some such embodiments, PGS 1202 may use a sliding window
to match the pattern example of the pattern in the set of patterns
with at least a portion of the graph image. In various embodiments,
PGS 1202 may compute an ROI correlation score in the set of ROI
correlation scores for each of the plurality of positions.
In one or more embodiments, PGS 1202 may retrieve a set of pattern
models based on the set of candidate ROIs of the graph image. In
some embodiments, each candidate ROI in the set of candidate ROIs
may be associated with one pattern model in the set of patterns. In
various embodiments, each pattern model in the set of pattern
models may be associated with one pattern in the set of patterns.
In some embodiments, PGS 1202 may compare each candidate ROI in the
set of candidate ROIs to an associated pattern model in the set of
pattern models to determine a set of pattern model correlation
scores. In one or more embodiments, the set of pattern model
correlation scores may include a pattern model correlation score
for each candidate ROI of the one or more candidate ROIs. In some
embodiments, each pattern model correlation score may indicate a
likelihood of a respective candidate ROI of the one or more
candidate ROIs including an associated pattern.
In various embodiments, PGS 1202 may identify one or more detected
patterns based on the set of pattern model correlation scores. In
various such embodiments, PGS 1202 may retrieve one or more text
templates based on the one or more detected patterns. In some
embodiments, the one or more text templates may include at least
one portion of text associated with each detected pattern of the
one or more detected patterns. In various embodiments, each text
template of the one or more text templates may be associated with a
priority level. In one or more embodiments, PGS 1202 may detect a
portion of the graph image with contextual information. In one or
more such embodiments, PGS 1202 may extract a textual element from
the portion of the graph image with contextual information. In some
embodiments, PGS 1202 may insert at least a portion of the
contextual information into at least one text template of the one
or more text templates to generate the textual description of the
graph image. In various embodiments, PGS 1202 may identify a
component of the graph image based on the graph type. In various
such embodiments, PGS 1202 may determine contextual information is
absent from the portion of the graph image with potential
contextual information based on the component of the graph image
identified based on the graph type. In one or more embodiments, PGS
1202 may detect a portion of the graph image with contextual
information.
In some embodiments, PGS 1202 may arrange the one or more text
templates in an order based on the priority level associated with
each text template to generate a textual description of the graph
image. In one or more embodiments, the summary of the graph image
may include the graph image and the textual description of the
graph image. In various embodiments, PGS 1202 may present the one
or more text templates arranged based on the priority level
associated with each text template via a user interface. In various
such embodiments, PGS 1202 may arrange the one or more text
templates in an updated order based on input received via the user
interface. In some such embodiments, PGS 1202 may alter a priority
level of at least one of the one or more text templates based on
the updated order. In one or more embodiments PGS 1202 may alter
the priority level of text template based on input received via a
user interface. In some embodiments, PGS 1202 may generate the
textual description based on the priority level associated with
each text template. In various embodiments, the priority level of
the at least one of the one or more text templates altered based on
the updated order. In one or more embodiments, PGS 1202 may produce
a summary of the graph image.
In various embodiments, PGS 1202 may receive an additional pattern
example. In various such embodiments, PGS 1202 may update a pattern
model in the set of pattern models based on the additional pattern
example. In some embodiments, at least one pattern in the set of
patters may comprise a personalized pattern. In one or more
embodiments, PGS 1202 may create the personalized pattern based on
one or more example graph images and one or more pattern examples
identified in the example graph images based on input received via
a user interface. In embodiments, PGS 1202 may associate one or
more of a priority level, a text template, or a graph type with the
personalized patter based on input received via the user interface.
The embodiments are not limited in the context of these operations.
It will be appreciated that these operations are not limiting, and
different or additional operations maybe performed by PGS 1202 in
generating personalized summary 1204 from input 1201 without
departing from the scope of this disclosure.
FIGS. 13A-13H illustrate operations of that may be performed by the
personalized pattern creator (PPC) 1252. In various embodiments,
the operations may create a personalized pattern that PGS 1201 can
identify and summarize. In various such embodiments, the operations
may include one or more of pre-processing input, extracting
features, updating one or more collections, training classifiers,
or updating a pattern dictionary. For instance, example graph
images, each comprising a common personalized pattern to be created
or updated, may be received as input and pre-processed. In such
instances, after pre-processing, features may be extracted from the
example graph images. In some embodiments, the extracted features
may include one or more mathematical representations of the example
graph images. In one or more embodiments, the example graph images
may then be used to update or create a new set of example graph
images associated with the personalized pattern. In one or more
such embodiments, a classifier may then be trained on the updated
or new set of example graph images. In various embodiments, the
classifier may then be used to update or create a pattern model in
a pattern model dictionary. In various such embodiments, the
pattern model may comprise the classifier. In some embodiments,
once the updated or created pattern model is stored in the pattern
model dictionary, PGS 1202 may be able to identify and summarize
it. Embodiments are not limited in this context.
Referring to FIG. 13A, PPC 1252 may identify or receive input 1301
for creating a personalized pattern to add to the pre-defined
patterns that PGS 1202 may detect and summarize. In some
embodiments, for instance, to create a personalized pattern, input
1301 may include one or more of example graph images,
identification of ROIs, an insight message (e.g., "higher outlier
(spike)"), a text template (e.g., A steading increase in data
points with a higher outlier or spike"), a graph type (e.g., line
chart), or a priority level (e.g., high). In some such embodiments,
PPC 1252 may take this information and learn the personalized
pattern and save it to a dictionary of pre-defined patterns or
pattern models. In one or more embodiments, PPC 1252 may learn a
pattern by generating a pattern model based on characteristics of
the pattern examples. In one or more such embodiments, PPC 1252 may
utilize a machine learning algorithm, such as in a Deep Neural
Network (DNN), to learn a pattern. In various embodiments, the
example images may also be used to improve graph type
identification. For example, the additional graph-type examples
provide more data to train a graph-type classifier, and the more
data available for training can result in an improved
classifier.
In various embodiments, PPC 1252 may include pre-processor 1302,
feature extractor 1304, collection updater 1306, classifier trainer
1308, and pattern dictionary updater 1310. Generally, operation of
PPC 1252 may proceed as follows. In one or more embodiments,
pre-processor 1302 may generate one or more regions of interest
(ROIs) associated with input 1301. In one or more such embodiments,
the ROIs may be generated based on highlighted regions of images in
input 1301. For instance, an ROI may include a spike in values
highlighted in a graph image provided as input 1301. In some
embodiments, pre-processor 1302 may pre-process input 1301. In some
such embodiments, pre-processing may include one or more of
denoising, resizing, or adjusting orientation of images. For
example, images in input 1301 may be resized to a first standard
size while ROIs are resized to a second standard size. In various
embodiments, feature extractor 1304 may extract characteristic
features associated with input 1301 (e.g., from the one or more
ROIs). In various such embodiments, this may include one or more of
raw pixels or a histogram of oriented gradients. In one or more
embodiments, collection updater 1306 may update one or more of a
graph-type examples collection or a pattern examples collection.
For instance, images in input 1301 may be added to the graph-type
examples collections and ROIs associated with input 1301 may be
added to the pattern examples collection. In various embodiments,
classifier trainer 1308 may generate one or more of a graph-type
classifier or a pattern model classifier. In various such
embodiments, the graph-type classifier and/or pattern model
classifier may be generated based on one or more of feature
characteristics, characteristics mapping, graph-type examples
collection, or pattern examples collection. In some embodiments,
pattern dictionary updater 1310 may update one or more of a
graph-type models collection or a pattern models collection based
on the classifiers generated by classifier trainer 1308. In one or
more embodiments, the pattern models collection may include a
pattern model for each pre-defined pattern that PGS 1202 may
detect. In some embodiments, the pattern models collection may
include a pattern model for one or more personalized patterns.
Referring to FIG. 13B, in some embodiments, input 1301 may include
example images 1320, graph-type 1326, insight message 1328, text
template(s) 1330, and priority level 1332. Further, example images
1320 may include one or more original images 1322 and one or more
highlighted images 1324. In various embodiments described herein,
original images 1322 may include a data visualization. As shown in
FIG. 13C, original image 1322 may include a clean graph image while
highlighted image 1324 includes the graph image with a bounding box
around ROI 1334. It will be appreciated that a user may be prompted
for one or more components of input 1301 at different times. For
instance, PPC 1252 may provide an interface that allows a user to
highlight ROI 1334 on original image 1322 to create highlighted
image 1324. In some embodiments, input 1301 may include different
or additional components.
Referring to FIG. 13D, in various embodiments, pre-processor 1302
may generate one or more ROIs (e.g., ROI 1334) associated with
input 1301. For instance, an ROI 1334 may include a spike in values
of a graph image provided as input 1301 (see e.g., FIG. 13C). In
various embodiments, pre-processor 1302 may extract ROI 1334 from
highlighted image 1324. In some embodiments, ROI 1334 may be
extracted from highlighted image 1324 by removing all portions of
original image 1322 except ROI 1334. In embodiments that multiple
ROIs are included in highlighted image 1324, pre-processor 1302 may
extract each ROI as an independent object. In various embodiments,
pre-processor 1302 may pre-process input 1301. In various such
embodiments, pre-processing may include one or more of denoising,
resizing, or adjusting orientation of images. In one or more
embodiments, pre-processor 1302 may resize one of more of example
images 1320 or ROI 1334 to one or more standard sizes. For example,
original image(s) 1322 may be resized to a first standard size
while ROIs (e.g., ROI 1334) are resized to a second standard
size.
Moving now to FIG. 13E, in various embodiments, feature extractor
1304 may extract characteristic features associated with input
1301, such as from one or more of original image 1322 or ROI 1334.
In various such embodiments, this may include one or more of raw
pixels or a histogram of oriented gradients. In some embodiments,
features may be mathematical representations of images. In one or
more embodiments, these features may be used to describe
characteristics of a pattern. In some embodiments, ROI 1334 may be
broken down into features 1336. In some such embodiments, one or
more of feature characteristics 1338 or characteristics mapping
1340 may be generated based on features 1336. In various
embodiments, PGS 1202 may utilize any combination of feature sets
having rotation and scale invariant descriptors (e.g., raw pixels).
In various such embodiments, rotation and scale invariant features
(e.g., densely sampled histogram of oriented gradients (HOG)) may
enable PGS 1202 to handle rotated graphs as well as graphs at
different scales.
Referring now to FIG. 13F, collection updater 1306 may use original
image(s) 1322 to create, update, and/or maintain graph-type
examples collection 1342 and ROI 1334 to create, update, and/or
maintain pattern examples collection 1344. In various embodiments,
original image 1322 may be stored under a graph-type identified in
input 1301 (e.g., graph-type 1326) as a graph-type example (e.g.,
graph-type example(s) 1347-1, 1347-2, 1347-n). In some embodiments,
original image 1322 may cause a new graph type to be created in
graph-type examples collection 1342. As shown in FIG. 13F,
graph-type examples collection 1342 may include any number of graph
types 1346-1, 1346-2, 1346-n under which original images 1322 may
be stored as or added to as graph type examples (e.g., graph-type
example(s) 1347-1, 1347-2, 1347-n). Further, each graph-type may
include any number of graph type examples.
Similarly, pattern examples collection 1344 may be stored in
pattern examples collection 1344 as a pattern example in a set of
one or more pattern examples (e.g., pattern example(s) 1350-1,
1350-2, 1350-n, 1354-1, 1354-2, 1354-n, 1356-1, 1356-2, 1356-n). In
one or more embodiments, ROI 1334 is stored in a set of pattern
examples under one or more classifications or associations. For
instance, each set of pattern examples may be associated with a
specific pattern (e.g., pattern 1348-1, 1348-2, 1348-n, 1352-1,
1352-2, 1352-n, 1356-1, 1356-2, or 1356-n) and a specific graph
type (e.g., graph type 1346-1, 1346-2, or 1346-n). In various
embodiments, collection updater 1306 create one or more of a new
graph type, pattern, or pattern example to accommodate ROI 1334 in
pattern examples collection 1344.
In FIG. 13G, classifier trainer 1308 may use one or more of feature
characteristics 1338, characteristics mapping 1340, graph-type
examples collection 1342, or pattern examples collection generate
or train one or more of graph-type classifier 1366 or a pattern
model classifier 1368. In various such embodiments, the graph-type
classifier 1366 and/or pattern model classifier 1368 may be
generated based on one or more of feature characteristics,
characteristics mapping, graph-type examples collection, or pattern
examples collection. In some embodiments, graph-type classifier
1366 may be generated based on or trained with one or more example
images (e.g., graph-type examples 1347-1, 1347-2, 1347-n) received
during operation of PPC 1252 and/or a pre-existing collection of
graph type images. For instance, PGS 1202 may come preloaded with
the pre-existing collection of graph type images. In various
embodiments, pattern model classifiers 1368 may be generated in a
similar manner to that of graph-type classifier 1366, except based
on different example images (e.g., pattern examples 1350-1 through
1358-n).
In one or more embodiments, classifier trainer 1308 may utilize
machine learning algorithms to generate or train one or more of
graph-type classifier 1366 and pattern model classifier 1368. For
example, a machine learning algorithm may take pattern examples
associated with a specific pattern (e.g., pattern examples 1350-1
of pattern 1348-1) as input and generate a corresponding pattern
model classifier as output. In one or more such embodiments, these
machine learning algorithms may utilize one or more of a Support
Vector Machine (SVM), a Deep Neural Network (DNN), Random Forest
Trees, Naive Baes, Boosting, and other machine learning algorithms
or techniques. In various embodiments, PGS 1202 may initially
support a set of initial graph types, such as linear graphs and bar
graphs. However, in some embodiments, the flexible and modular
design of PGS 1202 may support learning further graph types.
Further, in one or more embodiments, updating graph-type examples
collection 1342 and pattern examples collection 1344 with
additional examples may improve the accuracy of the generated
classifiers (e.g., graph-type classifier 1366 and/or pattern model
classifier 1368) by enabling a larger training set to be input to
the machine learning algorithm that generates the classifiers.
Moving to FIG. 13H, in some embodiments, pattern dictionary updater
1310 may create, update, and/or maintain one or more portions of a
model dictionary 1370. In various embodiments, model dictionary
1370 may include one or more of a graph-type models collection 1380
or a pattern models collection 1382 that are created, updated,
and/or maintained based on the classifiers generated by classifier
trainer 1308. In some embodiments, a graph-type model may comprise
an associated graph-type classifier. For instance, graph-type model
1372-1 for graph type 1346-1 may include a graph-type classifier
trained on graph-type examples 1347-1. Similarly, in various
embodiments, a pattern model may comprise an associated pattern
model classifier. For example, pattern model 1376-1 for pattern
1352-1 may include a pattern model classifier trained on pattern
examples 1354-1. In one or more embodiments, the pattern models
collection 1382 may include a pattern model for one or more of the
pre-defined patterns that PGS 1202 may detect (e.g., pattern
1348-1, 1348-2, 1348-n, 1352-1, 1352-2, 1352-n, 1356-1, 1356-2,
1356-n). In some embodiments, the patterns may be grouped by graph
type (e.g., graph type 1346-1, 1346-2, 1346-n). In various
embodiments, the graph-type models collection 1380 may include a
graph-type model (e.g., graph-type model 1372-1, 1372-2, 1372-n)
for each graph type that PGS 1202 can identify. In one or more
embodiments, the models may be used to generate a numerical
likelihood that an associated pattern or graph-type is present in
an image provided to PGS 1202 for generation of a personalized
summary (e.g., personalized summary 1204).
FIGS. 14A-14G illustrate operations of that may be performed by the
visual pattern detector (VPD) 1251. In various embodiments, the
operations may include of one or more of graph-type identification,
candidate ROI detection, pattern detections, and quality analysis.
Referring to FIG. 14A, VPD 1251 may identify or receive input 1201
to search for one or more patterns to enable generation of
personalized summary 1204. In some embodiments, such as the
embodiment of FIG. 14B, input 1201 may include an image 1410 (i.e.,
data visualization) that VPD 1251 searches for one or more
pre-defined patterns. In the illustrated embodiments, image 1410
includes a graph image. Embodiments are not limited in this
context.
In one or more embodiments described herein, the detection of
patterns in image 1410 by VPD 1251 may proceed as follows. Classify
the graph type in image 1410 based on one or more graph-type models
in graph-type models collection 1380 (e.g., graph-type models
1372-1, 1372-2, 1372-n). Retrieve a list of patterns for the graph
type of the image 1410. For instance, if the graph type of image
1410 is identified as graph type 1346-1, the list of patterns may
include patterns 1348-1, 1348-2, 1348-n. For each pattern
associated with the identified graph type of image 1410, the
associated set of pattern example(s) may be retrieved from pattern
examples collection 1344 and for each pattern example, a determined
number of ROIs in image 1410 that have high correlations with a
respective pattern example may be selected. For instance, the five
ROIs in image 1410 that correlate with each respective pattern
example the closest may be example may be selected. For each
pattern example, the selected ROIs may then be run against the
pattern model associated with the same pattern as the pattern
examples to determine a likelihood of detecting a respective
pattern in each of the respective selected ROIs. Finally, the
likelihoods are evaluated to identify one or more detected patters.
In various embodiments, the detected patterns may be passed to
summary generator 1254 for generation of personalized summary 1204
based on the detected patterns.
Referring to FIG. 14C, graph-type identifier 1402 may be
responsible for identifying and/or associating a graph-type with
image 1410. In one or more embodiments, associating a graph-type
with image 1410 may enable one or more patterns specific to that
graph type to be retrieved, such as from one or more of model
dictionary 1370, graph-type examples collection 1342, or pattern
examples collection 1344. In various embodiments, graph type
identifier may include a graph-type correlation score assessor
(GTCSA) 1412. In some embodiments, GTCSA 1412 may compute a
graph-type correlation score for one or more of graph types 1346-1,
1346-2, 1346-n (e.g., graph-type correlation scores 1422-1, 1422-2,
1422-n) based on image 1410. In various embodiments, GTCSA 1412 may
utilize one or more graph-type classifiers generated by classifier
trainer 1308 (e.g., graph-type classifier 1366) to compute the
graph-type correlation scores. For instance, GTCSA 1412 may run
image 1410 against each graph-type model in graph-type models
collection 1412 to generate a graph-type correlation score
associated with image 1410 for each respective graph type. In such
instances, a graph-type classifier trained on examples of a
respective graph type may be used to generate the graph-type
correlation score for the respective graph type. In one or more
embodiments, graph-type identifier 1402 may include a graph-type
correlation score evaluator (GTCSE) 1424. In one or more such
embodiments, GTCSE 1424 may associate image 1410 with an identified
graph-type 1426 based on a comparison of the graph-type correlation
scores.
Proceeding to FIG. 14D, the identified graph-type 1426 may be
passed to candidate ROI detector 1404. In one or more embodiments,
candidate ROI detector 1404 may identify candidate ROIs by
comparing example images of a pattern with image 1410. In one or
more such embodiments, this may include matching and/or computation
of a normalized correlation score between image 1410 and a
respective example image. The normalized correlation scores may
indicate a similarity between the example images of a pattern and
image 1410. In some embodiments, a matchlmage action may be
utilized for this purpose.
In some embodiments, matching that includes a sliding window method
may be utilized to compute correlation scores for the
identification of candidate ROIs 1432. For instance, portions or
patches of an example image (e.g., pattern example 1350-2) may be
overlaid on image 1410 in a plurality of positions, such as by
being slid horizontally and vertically over image 1410. In such
instances, a correlation score may be computed for each patch
position (i.e., each of the plurality of positions), and candidate
ROIs 1432 may be identified as patch positions with an associated
correlation score that satisfies one or more criteria. In various
embodiments, the sliding window method may be utilized when image
1410 is larger than the example images. In various embodiments, the
correlation scores may be compared to a threshold to determine
candidate ROIs 1432. In other embodiments, the correlation scores
may be compared to each other to determine candidate ROIs 1432. For
example, the top five correlation scores for each example image may
be selected as candidate ROIs 1432.
In one or more embodiments, candidate ROI detector 1404 may
retrieve one or more pattern examples associated with the
identified graph-type 1426 from pattern examples collection 1344
(e.g., pattern example(s) 1450-1, 1450-2, 1450-n. For instance, if
identified graph-type 1426 corresponds to graph type 1346-2,
pattern examples 1354-1, 1354-2, 1354-n may be retrieved from
pattern examples collection 1344.
In some embodiments, candidate ROI detector 1404 may include ROI
confidence score assessor (RCSA) 1428 and ROI confidence score
evaluator (RCSE) 1430. In various embodiments, RCSA 1428 may
compute one or more confidence scores associated with each of
pattern examples 1450-1, 1450-2, 1450-n. In various such
embodiments, RCSE 1430 may determine one or more candidate ROIs
1432 based on the confidence scores. For example, for each pattern
associated with the identified graph type 1426 of image 1410, the
associated set of pattern example(s) may be retrieved from pattern
examples collection 1344 and for each pattern example, a determined
number of ROIs in image 1410 that have high correlations with a
respective pattern example may be selected as candidate ROIs 1432.
In a further example, the five ROIs in image 1410 that correlate
with each respective pattern example the closest may be selected as
candidate ROIs 1432.
In one or more embodiments, pseudo code for operations performed by
candidate ROI detector 1404 may include one or more of:
TABLE-US-00001 1: listOfPatterns .rarw. getPatterns(graph-type) 2:
I .rarw. input image 3: k .rarw. number of best ROIs listOfROI
.rarw. [ ] 4: for each pattern p in listOfPatterns do 5: for each
example image i of p do 6: l .rarw. detectCandidateROI(I, i, k) 7:
listOfROI.append(l) 8: end for 9: end for
Moving to FIG. 14E, the candidate ROIs 1432 may be passed to
pattern detector 1406. In various embodiments, pattern detector
1406 may retrieve one or more pattern models associated with the
identified graph-type 1426 and each of the patterns associated with
the candidate ROIs 1432 from pattern models collection 1382 (e.g.,
pattern models 1448-1, 1448-2, 1448-n. In some embodiments, pattern
detector 1406 may include pattern model correlation score assessor
(PMCSA) 1434. In various embodiments, PMCSA 1434 may compute one or
more confidence or correlation scores associated with each of
pattern models 1448-1, 1448-2, 1448-n to determine one or more
candidate pattern(s) 1436 in image 1410. In one or more
embodiments, PMSCA 1434 may utilize one or more of pattern model
classifiers 1368 to compute the correlation scores associated with
each of the pattern models. For example, a pattern model classifier
trained on examples of a respective pattern may be used to generate
the pattern model correlations score for the respective
pattern.
In FIG. 14F, one or more candidate patterns 1426 along with their
respective pattern model correlation scores 1438 may be passed to
quality analyzer 1408. In some embodiments, quality analyzer 1408
may include a pattern model correlation score evaluator (PMCSE)
1440. In some such embodiments, PMCSE 1440 may determine one or
more detected patterns 1444 associated with image 1410 based on an
evaluation of the pattern model correlation scores. For example, in
FIG. 14G, candidate pattern 1436-1 may be associated with a pattern
model correlation score 1438-1 of 0.06, candidate pattern 1436-2
may be associated with a pattern model correlation score 1438-2 of
0.03, and candidate pattern 1436-n may be associated with a pattern
model correlation score 1438-n of 0.8. In such examples, PMCSE 1440
may select candidate pattern 1426-n as a detected pattern 1444-1 in
detected patterns 1444 in response to determining candidate pattern
1436-n has a pattern model correlation score that meets or exceeds
a threshold. In other words, candidate patterns that are not
associated with a strong enough pattern model correlation score may
be removed prior to passing the detected patterns 1444 to summary
generator 1254. In various embodiments, instead of a threshold, a
classifier (similar to graph-type classifier 1366 and/or pattern
model classifier 1368) may be trained to determine whether a
candidate pattern should be selected as a detected pattern. In
various such embodiments example data including a set of pattern
model correlation scores and an indication of whether the candidate
pattern associated with each of the correlation scores was selected
as a detected pattern may be used to train the classifier.
FIG. 15 illustrates an example of a processing flow 1500 of summary
generator 1254 that may be representative of various embodiments.
In processing flow 1500, summary generator 1254 may include
document planner 1502, sentence constructor 1504, and text
generator 1506. In some embodiments, summary generator 1254 may
produce personalized summary 1204 based on detected patterns 1444.
In some such embodiments, summary generator 1254 may generate
personalized summary 1204 based on information received from one or
more of context extractor 1258 and summary personalizer 1256. In
one or more embodiments, summary generator 1254 may perform natural
language processing. For instance, sentence constructor 1504 and
context extractor 1258 may generate natural language from context
extracted from an example image. Embodiments are not limited in
this context.
As previously described, each pattern may be associated with an
insight message and one or more text templates. In various
embodiments, the one or more text templates may be designed to
express in natural language the insight included in the insight
message. In various such embodiments, this may be in combination
with other closely related insights. In some embodiments, summary
generator 1254 may begin with document planner 1502. In one or more
embodiments, document planner 1502 may arrange the insight messages
in a logical manner (e.g., based on or more of a priority level or
user preferences). Document planner 1502 may then pass the arranged
insight messages, for instance, as a group, to sentence constructor
1504.
In one or more embodiments, sentence constructor 1504 may match the
arranged insight messages to one or more text templates. In various
embodiments, sentence constructor 1504 may insert one or more
portions of context identified by context extractor 1258 into one
or more of the text templates. For example, a sample text template
may be "A steady increase in data points with a higher outlier or
spike." In such examples, `data points` may not always be part of
the text template, and instead it may be replaced with relevant
context identified by context extractor 1258. Thus, with the
appropriate context, the sample text template may be modified to be
"A steady increase in dollar amount over time with a higher outlier
or spike."
In some embodiments, PGS 1202 may be designed to work with whatever
information is available. Accordingly, a text template may remain
as is if context is unavailable. However, with the availability of
context, either from a user or extracted by context extractor 1258,
a text template may be converted into a more fine-grained text
template based on the context. In various embodiments, text
generator 1506 may check the grammatical correctness of the text
templates and adds any needed markup in order to produce
personalized summary 1204. In one or more embodiments, text
generator 1506 may add one or more markups or make one or more
revisions based on input received via summary personalizer
1256.
FIG. 16 illustrates an example of a processing flow 1600 of context
extractor 1258 that may be representative of various embodiments.
In processing flow 1600, context extractor 1258 may include text
detector 1602, text recognizer 1604, and text validator 1606. In
one or more embodiments described herein, context extractor 1258
may detect a portion of input 1601 with contextual information. In
one or more such embodiments, context extractor 1258 may generate
or extract context 1608 (e.g., textual elements) from the portion
of input 1601 with contextual information. In some embodiments,
input 1601 be the same or similar to input 1201. Thus, in the
illustrated embodiments, input 1601 includes image 1410 and
identified graph-type 1426. In one or more embodiments context
extractor 1258 may utilize identified graph-type 1426 to extract
context 1608 from image 1410. In one or more such embodiments, know
the graph type may provide prior knowledge of the components of an
image, such as the location of axes, and data labels. In various
embodiments, context extractor 1258 may utilize optical character
recognition (OCR) and/or computer vision. Embodiments are not
limited in this context.
In one or more embodiments, context extraction may include multiple
steps. For instance, text detector 1602 may detect contextual
information, such as textual elements in image 1410. In some
embodiments, text recognizer 1604 may then determine whether the
detected textual elements include one or more of a title, a name,
an axis label, or a legend, and extract the detected textual
elements. In various embodiments, text validator 1606 may be
included in context extractor 1258 to provide the ability to
request verification from a user for the proper recognition of
textual elements. In some embodiments, text validator 1606 may
enable a user to modify the extracted textual elements to improve
accuracy of context extractor 1258.
As context extractor 1258 knows the graph type of image 1410, it
also has prior knowledge of the graph, and may use this information
to rectify a text detection algorithm to avoid false positives. In
some instances, detected text blobs (the output of text detector
1602) may then be feed to text recognizer 1604. In various
embodiments, a text recognition model may be created offline based
on one or more known, supported, and non-cursive font types, such
as Arial. After detection, recognition, extraction, and, if
necessary, verification, context extractor 1258 may parse the
information extracted from image 1410 so that PGS 1202 may consume
it as context 1608. For instance, a data label such as S10,000,000
may become "dollar amount". Similarly, "Nov-16" and "Mar-17" may
become "over time".
In a further example, a graph image with revenue on the y-axis and
year on the x-axis may be received for summarization. In such
examples, context extractor 1258 may identify that the values on
the y-axis may include a dollar sign (e.g., `S`) and the values on
the x-axis may be four digit numbers (e.g., 2014, 2015, 2016).
Based on this information, context extractor 1258 may determine
that the y-axis represents some type of resource information and
the x-axis represents the year. In various embodiments, context
extractor 1258 may identify a title of `Revenue v. Year` in the
graph image. In various such embodiments, the context extractor
1258 may determine the type of monetary information represented by
the y-axis is revenue. In one or more embodiments, this information
may enable the summary to be detailed. For instance, rather than a
summary that includes "a linear increase", a detailed summary that
includes "a linear increase in revenue by year". In another
instance, context extractor 1258 may identify axis labels, such as
"home price" and "square foot". In such other instances, the axis
labels may be used to contextually enrich the summary In various
embodiments described herein, more contextual information extracted
from the graph image may result in a more detailed summary.
FIG. 17 illustrates an example processing flow of a summary
personalizer 1256 that may be representative of various
embodiments. In processing flow 1700, summary personalizer 1256 may
include user interface 1702 and preference manager 1704. In some
embodiments, summary personalizer 1256 may enable a user to
interact with PGS 1202 by personalizing a generated summary text.
In some such embodiments, preference manager 1704 may utilize these
interactions to learn one or more preferences of a user. After the
user interaction, the revised summary text may become personalized
summary 1204. Embodiments are not limited in this context.
In one or more embodiments, PGS 1202 may detect more than one
pattern in input 1201, resulting in more than one text templates in
the summary text. In one or more such embodiments, the multiple
text templates may be arranged according to a priority level. In
some embodiments, the priority level defined in input 1301 may be
used to arrange the multiple text templates. As previously
mentioned, the arrangement may initially be carried out by document
planner 1502. In various embodiments, PGS 1202 is not limited to a
static ordering schema based on static priority levels. In various
such embodiments, instead of a static ordering schema, a flexible
or dynamic ordering schema may be utilized to generate personalized
summaries with natural-language text.
For instance, each text template may start with an assigned or
initial priority level. In such instances, the assigned priority
levels may be dynamically updated based on user preferences.
Upgrading a text template may cause the priority level associated
with the text template to be raised. Similarly, downgrading a text
template may cause the priority level associated with the text
template to be lowered. In some embodiments, a user may delete any
of the generated text. In one or more embodiments, this information
may be fed to document planner 1502 to be used in future
summarizations. Accordingly, in various embodiments, PGS 1202 may
enable a to tailor the generated natural-text summary (e.g.,
personalized summary 1204) based on one or more user preferences.
In one or more embodiments, the personalization capability for the
ordering of text templates may be the same or similar to
content-based filtering, such as in recommender systems.
FIG. 18 illustrates an embodiment of personalized summary 1204.
Personalized summary 1204 may include image 1410 and textual
description 1850 with a plurality of text templates (e.g., text
templates 1330-1A, 1330-1B, 1330-2. In the illustrated embodiments,
text template 1330-1A may be placed first in the textual
description 1850 of the personalized summary 1204 based on priority
level 1332-1 being higher than priority levels 1332-2, 1332-3.
Similarly, text template 1330-2 may be placed second based on
priority level 1332-2 being higher than priority level 1332-3, but
lower than priority level 1332-1. Further, context 1608-1 may be
inserted into text template 1330-1A, context 1608-2 may be inserted
into text template 1330-2, and contexts 1608-3A, 1608-3B may be
inserted into text template 1330-1B. Embodiments are not limited in
this context.
FIGS. 19A-19B illustrates an embodiment of a logic flow 1900. The
logic flow 1900 may be representative of some or all of the
operations executed by one or more embodiments described herein.
More specifically, the logic flow 1900 may illustrate operations
performed by processing circuitry 1220, and/or performed by other
component(s) of personalized graph summarizer 1202, such as visual
pattern detector 1251, personalized pattern creator 1252, summary
generator 1254, summary personalizer 1256, or context extractor
1258. In one or more embodiments, these operations may be performed
in conjunction with generating a personalized summary 1204 or
learning a new graph-type or pattern model. Embodiments are not
limited in this context.
In the illustrated embodiment shown in FIGS. 19A-19B, the logic
flow 1900 may begin at block 1902. At block 1902 a data
visualization comprising a graph image may be identified. For
instance, visual pattern detector 1251 may identify a data
visualization comprising a graph image in input 1201. In some
embodiments, the data visualization comprising the graph image may
be an image file received as input 1201. In various embodiments,
identification of the data visualization may be automated.
Continuing to block 1904, a set of graph-type correlation scores
with a graph-type correlation score for each graph type of a
plurality of graph types may be determined. In logic flow 1900,
each graph type correlation score may be based on a comparison of
at least a portion of the graph image with one or more graph-type
models associated with each graph type of the plurality of graph
types. For instance, graph-type identifier 1402 may include GTCSA
1412 to compare the graph image (e.g., 1410) to each of graph types
1346-1, 1346-2, 1346-n using graph-type models 1372-1, 1372-2,
1372-n, respectively. In such instances, one or more of graph-type
models 1372-1, 1372-2, 1372-n may include graph-type classifier
1366 or another graph-type classifier generated by classifier
trainer 1308. In some embodiments, each graph-type model may take
the graph image as input and output an associated graph-type
correlation score. For example, GTCSA 1412 may provide image 1410
as input to graph-type model 1372-2 to generate graph-type
correlation score 1422-2 for graph type 1346-2. In such examples,
similarly GTCSA 1412 may generate graph-type correlation score
1422-1 for graph type 1346-1 using graph-type model 1372-1 and
graph-type correlation score 1422-n for graph type 1346-n using
graph-type model 1372-n. In one or more embodiments, determination
of the set of graph-type correlation scores may be automated.
Proceeding to block 1906, the set of graph-type correlation scores
may be evaluated to identify a graph type of the graph image. In
some embodiments, graph-type correlation score evaluator 1424 may
compare graph-type correlation scores 1422-1, 1422-2, 1422-n to
determine identified graph-type 1426. For instance, graph type
1346-2 may be selected as identify graph-type 1426 in response to
having the highest graph-type correlation score. In various
embodiments, identification of the graph type of the graph image
may be automated.
At block 1908 a set of patterns may be retrieved based on the graph
type of the graph image. In logic flow 1900, each pattern in the
set of patterns may include one or more pattern examples. For
instance, if graph type 1346-1 is selected as identified graph-type
1426, then patterns 1348-1, 1348-2, 1348-n may be retrieved from
pattern examples collection 1344. In such instances, pattern 1348-1
may include pattern examples 1350-1, pattern 1348-2 may include
pattern examples 1350-2, and pattern 1348-n may include pattern
examples 1350-n. In some embodiments, retrieval of the set of
patterns may be automated.
Continuing to block 1910, a set of region of interest correlation
scores may be determined for the graph image based on matching the
one or more pattern examples of each pattern in the set of patterns
with at least a portion of the graph image. In logic flow 1900, the
set of region of interest correlation scores may include at least
one region of interest correlation score for each pattern in the
set of patterns. For instance, ROI confidence score assessor 1428
may determine a confidence score for each of pattern example(s)
1450-1, 1450-2, 1450-n. In some embodiments, the matching may
utilize a sliding window method to compute correlation scores. For
example, portions or patches of the example image (e.g., pattern
example 1350-2) may be overlaid on image 1410 in a plurality of
positions, such as by being slid horizontally and vertically over
image 1410. In such instances, a correlation score may be computed
for each patch position (i.e., each of the plurality of positions).
In one or more embodiments, determination of the set of region of
interest correlation scores may be automated.
Proceeding to block 1912, the set of region of interest correlation
scores may be evaluated to identify one or more candidate regions
of interest comprising in the graph image with each candidate
region of interest comprising a portion of the graph image. In
various embodiments, ROI confidence score evaluator 1430 may
evaluate the set of region of interest correlation scores
determined by ROI confidence score assessor 1428 to identify
candidate ROI(s) 1432. In embodiments that utilize the sliding
window method to compute the ROI correlation scores, candidate ROIs
1432 may be identified as patch positions with an associated
correlation score that satisfies one or more criteria. In some
embodiments, the correlation scores may be compared to a threshold
to determine candidate ROIs 1432. In other embodiments, the
correlation scores may be compared to each other to determine
candidate ROIs 1432. For example, the top five correlation scores
for each example image may be selected as candidate ROIs 1432. In
various embodiments, identification of the one or more candidate
regions of interest may be automated.
At block 1914 a set of pattern models may be retrieved based on the
set of candidate regions of interest of the graph image. In logic
flow 1900 each candidate region of interest in the set of candidate
regions of interest may be associated with one pattern model in the
set of pattern models and each pattern model in the set of pattern
models may be associated with one pattern in the set of patterns.
In various embodiments, the set of pattern models may include the
pattern model for each pattern associated with a candidate region
of interest. For instance, if candidate ROIs 1432 include one
candidate ROI associated with pattern 1352-1 and one candidate ROI
associated with pattern 1352-2, then the set of pattern models
retrieved may include pattern model 1376-1 and pattern model
1376-2. In some embodiments, retrieval of the set of pattern models
may be automated. At block 1916 the logic flow 1900 may proceed to
block 1918 in FIG. 19B.
Continuing to block 1918, each candidate region of interest in the
set of candidate regions of interest may be compared to an
associated pattern model in the set of pattern models to determine
a set of pattern model correlation scores. In logic flow 1900, the
set of pattern model correlation scores may include a pattern model
correlation score for each candidate region of interest of the one
or more candidate regions of interest. For instance, graph-type
identifier 1406 may include PMCSA 1434 to compare each candidate
ROI in the set of candidate ROIs to an associated pattern model. In
such instances, PMCSA 1434 may operate the same or similar to GTCSA
1412, except with pattern models instead of graph-type models. In
various embodiments, determining the set of pattern model
correlation scores may be automated.
Proceeding to block 1920, one or more detected patterns may be
identified based on the set of pattern model correlation scores.
For instance, pattern model correlation score evaluator 1440 may
compare pattern model correlation scores 1438-1, 1438-2, 1438-n to
identify detected pattern(s) 1444. In some embodiments, the
correlation scores may be compared to a threshold to determine
detected pattern(s) 1444. In other embodiments, the correlation
scores may be compared to each other to determine detected
pattern(s) 1444. For example, the top five correlation scores may
be selected as detected pattern(s) 1444. In various embodiments,
identification of the one or more candidate regions of interest may
be automated.
At block 1922, one or more text templates may be retrieved based on
the one or more detected patterns. In logic flow 1900, the one or
more text templates may include at least one portion of text
associated with each detected pattern of the one or more detected
patterns, and each text template of the one or more text templates
may be associated with a priority level. For instance, if a
detected pattern corresponds to input 1301, text template 1330 may
be retrieved. In such instances, text template 1330 may be
associated with priority level 1332. In various embodiments,
retrieval of the one or more text templates may be automated.
Continuing to block 1924, the one or more text templates may be
arranged in an order based on the priority level associated with
each text template to generate a textual description of the graph
image. For instance, summary generator 1254 may operate to generate
textual description 1850 of the graph image based on the priority
levels associated with each text template. In various embodiments,
the textual description may be personalized based on input from
context extractor 1258 and/or summary personalizer 1256. In various
embodiments, generation of the textual description may be
automated. Proceeding to block 1926, a personalized summary
comprising the graph image and the textual description of the graph
image may be produced. For example, personalized summary 1204 may
be produced by summary generator 1254 that includes image 1410 and
textual description 1850 (see e.g., FIG. 18). In various
embodiments, generation of the textual description may be
automated. In some embodiments, production of the personalized
summary may be automated.
In various embodiments, processing circuitry 1220 may include any
of a wide variety of commercially available processors. Further,
one or more of these processors may include multiple processors, a
multi-threaded processor, a multi-core processor (whether the
multiple cores coexist on the same or separate dies), and/or a
multi-processor architecture of some other variety by which
multiple physically separate processors are linked.
However, in a specific embodiment, the processing circuitry 1220 of
system 1205 may be selected to efficiently perform the generations
of personalized summary 1204 based on input 1201. Alternatively, or
additionally, the processors of one or more node devices may be
selected to efficiently perform one or more operations described
herein. In some embodiments, one or more operations described
herein may be performed at least partially in parallel. By way of
example, the processing circuitry 1220 or other processors may
incorporate a single-instruction multiple-data (SIMD) architecture,
may incorporate multiple processing pipelines, and/or may
incorporate the ability to support multiple simultaneous threads of
execution per processing pipeline.
In various embodiments, one or more portions of the processing or
logic flows described herein, including the components of which
each is composed, may be selected to be operative on whatever type
of processor or processors that are selected to implement
applicable ones of the processing circuitry 1220 or other
processors utilized by PGS 1202. In various embodiments, each of
these one or more portions of the processing or logic flows
described herein may include one or more of an operating system,
device drivers and/or application-level routines (e.g., so-called
"software suites" provided on disc media, "applets" obtained from a
remote server, etc.). Where an operating system is included, the
operating system may be any of a variety of available operating
systems appropriate for processing circuitry 1220 or other
processors. Where one or more device drivers are included, those
device drivers may provide support for any of a variety of other
components, whether hardware or software components, described
herein.
In various embodiments, each of the storage 1215 and memory 1210
may be based on any of a wide variety of information storage
technologies, including volatile technologies requiring the
uninterrupted provision of electric power, and/or including
technologies entailing the use of machine-readable storage media
that may or may not be removable. Thus, each of these storages may
include any of a wide variety of types (or combination of types) of
storage device, including without limitation, read-only memory
(ROM), random-access memory (RAM), dynamic RAM (DRAM),
Double-Data-Rate DRAM (DDR-DRAM), synchronous DRAM (SDRAM), static
RAM (SRAM), programmable ROM (PROM), erasable programmable ROM
(EPROM), electrically erasable programmable ROM (EEPROM), flash
memory, polymer memory (e.g., ferroelectric polymer memory), ovonic
memory, phase change or ferroelectric memory,
silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or
optical cards, one or more individual ferromagnetic disk drives,
non-volatile storage class memory, or a plurality of storage
devices organized into one or more arrays (e.g., multiple
ferromagnetic disk drives organized into a Redundant Array of
Independent Disks array, or RAID array). It should be noted that
although each of these storages is depicted as a single block, one
or more of these may include multiple storage devices that may be
based on differing storage technologies. Thus, for example, one or
more of each of these depicted storages may represent a combination
of an optical drive or flash memory card reader by which programs
and/or data may be stored and conveyed on some form of
machine-readable storage media, a ferromagnetic disk drive to store
programs and/or data locally for a relatively extended period, and
one or more volatile solid state memory devices enabling relatively
quick access to programs and/or data (e.g., SRAM or DRAM). It
should also be noted that each of these storages may be made up of
multiple storage components based on identical storage technology,
but which may be maintained separately as a result of
specialization in use (e.g., some DRAM devices employed as a main
storage while other DRAM devices employed as a distinct frame
buffer of a graphics controller). However, in a specific
embodiment, the storage 1215 of one or more of the node may be
implemented with a redundant array of independent discs (RAID) of a
RAID level selected to provide fault tolerance to prevent loss of
one or more of these datasets and/or to provide increased speed in
accessing one or more of these datasets.
In various embodiments, one or more of the interfaces described
herein (e.g., interfaces 1225 or user interface 1702) may each be
any of a variety of types of input device that may each employ any
of a wide variety of input detection and/or reception technologies.
Examples of such input devices include, and are not limited to,
microphones, remote controls, stylus pens, card readers, finger
print readers, virtual reality interaction gloves, graphical input
tablets, joysticks, keyboards, retina scanners, the touch input
components of touch screens, trackballs, environmental sensors,
and/or either cameras or camera arrays to monitor movement of
persons to accept commands and/or data provided by those persons
via gestures and/or facial expressions. In various embodiments,
each of the displays 1580 and 1780 may each be any of a variety of
types of display device that may each employ any of a wide variety
of visual presentation technologies. Examples of such a display
device includes, and is not limited to, a cathode-ray tube (CRT),
an electroluminescent (EL) panel, a liquid crystal display (LCD), a
gas plasma display, etc. In some embodiments, one or more of the
interfaces may be a touchscreen display.
In various embodiments, interfaces 1225 of PGS 1202 may include one
or more network interfaces that employ any of a wide variety of
communications technologies enabling these devices to be coupled to
other devices as has been described. Each of these interfaces
includes circuitry providing at least some of the requisite
functionality to enable such coupling. However, each of these
interfaces may also be at least partially implemented with
sequences of instructions executed by corresponding ones of the
processors (e.g., to implement a protocol stack or other features).
Where electrically and/or optically conductive cabling is employed,
these interfaces may employ timings and/or protocols conforming to
any of a variety of industry standards, including without
limitation, RS-232C, RS-422, USB, Ethernet (IEEE-802.3) or
IEEE-1394. Where the use of wireless transmissions is entailed,
these interfaces may employ timings and/or protocols conforming to
any of a variety of industry standards, including without
limitation, IEEE 802.11a, 802.11ad, 802.11ah, 802.11ax, 802.11b,
802.11g, 802.16, 802.20 (commonly referred to as "Mobile Broadband
Wireless Access"); Bluetooth; ZigBee; or a cellular radiotelephone
service such as GSM with General Packet Radio Service (GSM/GPRS),
CDMA/1.times.RTT, Enhanced Data Rates for Global Evolution (EDGE),
Evolution Data Only/Optimized (EV-DO), Evolution For Data and Voice
(EV-DV), High Speed Downlink Packet Access (HSDPA), High Speed
Uplink Packet Access (HSUPA), 4G LTE, etc. However, in a specific
embodiment, a network interface of interfaces 1225 may be
implemented with multiple copper-based or fiber-optic based network
interface ports to provide redundant and/or parallel pathways in
exchanging data.
In various embodiments, the processing and/or storage resources of
PGS 1202 may be divided among the multiple systems. In various such
embodiments, one or more API architectures may support
communications among the multiple systems. The one or more API
architectures may be configured to and/or selected to conform to
any of a variety of standards for distributed processing, including
without limitation, IEEE P2413, AllJoyn, IoTivity, etc. By way of
example, a subset of API and/or other architectural features of one
or more of such standards may be employed to implement the
relatively minimal degree of coordination described herein to
provide greater efficiency in parallelizing processing of data,
while minimizing exchanges of coordinating information that may
lead to undesired instances of serialization among processes.
However, it should be noted that the parallelization of storage,
retrieval and/or processing of data among multiple systems is not
dependent on, nor constrained by, existing API architectures and/or
supporting communications protocols. More broadly, there is nothing
in the manner in which the data may be organized in storage,
transmission and/or distribution via network interface of
interfaces 1225 that is bound to existing API architectures or
protocols.
Some systems may use Hadoop.RTM., an open-source framework for
storing and analyzing big data in a distributed computing
environment. Some systems may use cloud computing, which can enable
ubiquitous, convenient, on-demand network access to a shared pool
of configurable computing resources (e.g., networks, servers,
storage, applications and services) that can be rapidly provisioned
and released with minimal management effort or service provider
interaction. Some grid systems may be implemented as a multi-node
Hadoop.RTM. cluster, as understood by a person of skill in the art.
Apache.TM. Hadoop.RTM. is an open-source software framework for
distributed computing.
* * * * *
References